The Fractures Which Are Non-detectable And Which Are Not Diagnosed Properly Are The Two Major Challenges To The Orthopedicians And Radiologists In The Field Of Orthopaedics. In Many Countries The Count Of Patients With Bone-related Fracture Issues Is Increasing Very Rapidly, Especially Many Are Meeting With An Accident. Additionally, Due To A Shortage In Rural Areas Where The Medical Facility Is Very Poor And Unavailability Of Orthopaedics Makes The Introduction Of Computer-based Systems. In This Paper, A Computer-Aided Fracture Detection Technique Is Proposed Using X-rays Which Detect And Locate The Fractures Very Efficiently And Lessens The Burden Of Orthopedicians. Bag Of Visual Words (BOVW) Technique Is Proposed Where Features Are Labelled As Fractured And Non-fractured With The Training Of Back Propagation Neural Network (BPNN) Classifier With The Features Extracted From SIFT(Scale Invariant Feature Transform). The Proposed Method Experiments Over 300 Xrays Images Which Are Collected As A Dataset From KIMS Hospital, Hyderabad. The Experimental Results Give 90% Accuracy Compared To HTBFD (Hought Transform Based Fracture Detection And ANN ( Artificial Neural Network) Which Can Be Used For Better Location Of Fractures.
CT Imaging Of Benign And Malignant Thyroid Tumor To Predict The Depth Of The Learning Algorithm, Built On Circulation Volume Product Thyroid CT Image Neural Network Forecasting Model. To Improved The Prediction Model, Constructed The Convolutional Neural Network Prediction Model And Optimized The Prediction Model. The Detection Is Done By The Single Shot Detection Algorithm. Soc Max Algorithm And L2 Regularization Are Introduced To Prevent The Occurrence Of Over Fitting. This Study Introduces The Technology And Tools Required For The Development Of Forecasting Systems, The Feasibility Analysis Of The System, Demand Analysis And System Design And Other System Development Preliminary Work. IT Describes The Function Of The Thyroid Tumor Prediction System And Related Work Such As System Testing. Based On The Above Research, Thyroid CT Images Obtained By The Cooperative Hospital Are Used As A Data Set, And The Cyclic Convolutional Neural Network Prediction Model Is Used To Predict Training And Testing To The Development Of A Thyroid Tumor Prediction System. The Experimental Results Show That The Prediction System Has High Prediction Accuracy.
Tuberculosis (TB) Is An Airborne Infectious Disease Caused By Organisms In The Mycobacterium Tuberculosis (Mtb) Complex. In Many Low And Middle-income Countries, TB Remains A Major Cause Of Morbidity And Mortality. Once A Patient Has Been Diagnosed With TB, It Is Critical That Healthcare Workers Make The Most Appropriate Treatment Decision Given The Individual Conditions Of The Patient And The Likely Course Of The Disease Based On Medical Experience. Depending On The Prognosis, Delayed Or Inappropriate Treatment Can Result In Unsatisfactory Results Including The Exacerbation Of Clinical Symptoms, Poor Quality Of Life, And Increased Risk Of Death. Automated Diagnosis Of Tuberculosis (TB) From Chest X-Rays (CXR) Has Been Tackled With Either Hand-crafted Algorithms Or Machine Learning Approaches. We Propose A Simple Support Vector Machines (SVMs), Multilayer Preceptron And Random Forest Optimized For The Problem Which Is Faster And More Efficient Than Previous Models But Preserves Their Accuracy.
Emotions Are Fundamental For Human Beings And Play An Important Role In Human Cognition. Emotion Is Commonly Associated With Logical Decision Making, Human Interaction, And To A Certain Extent, Human Intelligence Itself. With The Growing Interest Of The Research Community Towards Establishing Some Meaningful “emotional” Interactions Between Humans And Computers, The Need For Reliable And Deployable Solutions For The Identification Of Human Emotional States Is Required. EEG Is Considered A Physiological Clue In Which Electrical Activities Of The Neural Cells Cluster Across The Human Cerebral Cortex. EEG Is Used To Record Such Activities And Is Reliable For Emotion Recognition Due To Its Relatively Objective Evaluation Of Emotion Compared To Non Physiological Clues (facial Expression, Gesture, Etc). Works Describing That EEG Contains The Most Comprehensive Features Such As The Power Spectral Bands Can Be Utilized For Basic Emotion Classifications.
In Recent Years, The Most Prevalent Form Of Cancer Diagnosed In Women Across The Globe Is Breast Cancer. It Develops In The Breast Tissue And Is One Of The Most Frequent Causes Of Womens Death. This Cancer Can Be Cured If It Is Diagnosed At Preliminary Stage. Malignant And Benign Are Two Types Of Tumor Found In Case Of Breast Cancer. Malignant Tumors Are Deadly As Their Rate Of Growth Is Much Higher Than Benign Tumors. So, Early Identification Of Tumor Type Is Pivotal For The Appropriate Treatment Of A Patient Having Breast Cancer. In This Work, Wisconsin Breast Cancer Dataset Has Been Used Which Was Collected From UCI Repository. Our Goal Is To Analyze The Dataset And Evaluate The Performance Of Various Machine Learning Algorithms For Predicting Breast Cancer.
The Sleep Disorder Not Only Has An Influence On The Sleep Quality Of These Patients And Causes Serious Fatigue But Also Can Endanger These People’s Lives. Sleep Apnea Syndrome Is One Of The Most Common And Dangerous Causes Of Sleep Disorder. When OSA Occurs During Sleep, It May Cause Suspensions In The Patient’s Breathing. Since Human Body Rhythms Have A Chaotic And Non-linear Behavior, The Nonlinear Analysis Of Body Parameters Provides The Researchers With Valuable Information About Body Behavior During The Disease And Its Comparison With The Normal State For A More Accurate Examination Of The Diseases. The Purpose Of This Is To Diagnose Apnea Events Using Linear And Nonlinear Analyses And Combining The EMG, ECG And EEG Signals In Patients With Obstructive Sleep Apnea (OSA).
The Computed Tomography (CT) Scan Image Is The Most Reliable Method And Mostly Used In Radiotherapy As It Is Of Low Cost Yet Efficient Compared To The Magnetic Resonance Imaging (MRI) Scan. For Brain CT Scan Application Conversion Of Medical Images And Segmentation Techniques Are Essential. U-net Is A Neural Network Architecture Designed Primarily For Image Segmentation. These Traits Provide U-net With A High Utility Within The Medical Imaging Community And Have Resulted In Extensive Adoption Of U-net As The Primary Tool For Segmentation Tasks In Medical Imaging. The Success Of U-net Is Evident In Its Widespread Use In Nearly All Major Image Modalities, From CT Scans And MRI To X-rays And Microscopy. Given That U-net’s Potential Is Still Increasing, This Narrative Literature Review Examines The Numerous Developments And Breakthroughs In The U-net Architecture And Provides Observations On Recent Trends.
Brain Tumors Is The Most Common And Aggressive Leading To A Very Short Life Expectancy In Their Highest Grade. Brain Tumor Detection Is One Of The Most Difficult Tasks In Medical Image Processing. The Detection Task Is Difficult To Perform Because There Is A Lot Of Diversity In The Images As Brain Tumors Come In Different Shapes And Textures. Brain Tumors Arise From Different Types Of Cells And The Cells Can Suggest Things Like The Nature, Severity, And Rarity Of The Tumor. Tumors Can Occur In Different Locations And The Location Of Tumors Can Suggest Something About The Type Of Cells Causing The Tumor Which Can Aid Further Diagnosis. The Task Of Brain Tumor Detection Can Become Aggravating By The Problems Which Are Present In Almost All Digital Images. In This Paper, We Propose A Novel Brain Tumor Detection Method In MRI Images Using Image Processing And Deep Learning Algorithm.
Breast Cancer Is One Of The Most Widespread Diseases Among Women. As Per The Statistical Review Of The World Health Organization (WHO), 1.5 Million Women Are Affected Around The World By Carcinoma Cancer. In Recent Years, The Number Of Breast Cancer Patients Has Increased Rapidly. The Early Diagnosis Of Breast Cancer Is Very Important To Increase The Survival Rate. Hence, A Trustworthy Diagnosis And Detection Process Is Required. Automatic Detection Processes Will Be Very Helpful For Medical Practitioners. There Are Numerous Proposed Methods For Timely Sensing Of Breast Cancer. This Study Has Suggested A Deep Neural Network With Feature Selection Techniques To Predict Breast Cancer.
According To The World Health Organization (WHO), It Is Estimated That In 2019, Epilepsy Affects Around 50 Million People Worldwide Which Is Quite Common In Childhood. The Benefits Of Early Detection And Classification Of Epileptic Seizures In Analysis, Monitoring And Diagnosis For The Realization And Actualization Of Computer-aided Devices And Recent Internet Of Medical Things (IoMT) Devices Can Never Be Overemphasized. The Success Of These Applications Largely Depends On The Accuracy Of The Detection And Classification Techniques Employed. Several Methods Have Been Investigated, Proposed And Developed Over The Years. This Paper Investigates Various Seizure Detection Algorithms And Classifications In Deep Learning Algorithms. It Also Discusses Epileptiform Detection As One Of The Steps Towards Advanced Diagnoses Of Disorders Of Consciousness (DOCs) And Their Understanding.
The Retinal Image Diagnosis Is An Important Methodology For Diabetic Retinopathy Detection And Analysis. The Blood Vessel Detection And Segmentation Is An Important For Diabetic Retinopathy Diagnosis At Earlier Stage. In This Process The Morphological Operations And SVM Classifier Are Used To Detect And Segment The Blood Vessels From The Retinal Image. The System Consists Of Three Stages- First Is Preprocessing Of Retinal Image To Separate The Green Channel And Second Stage Is Retinal Image Enhancement And Third Stage Is Blood Vessel Segmentation Using Morphological Operations And SVM Classifier.
LUNG CANCER Is Most Dominant Reason For Cancer Related Deaths Across The Globe. The Uncontrollable Division Of Undesirable Cells In The Lung Region Can Be Classified As Lung Cancer. As Their Growth Progresses, The Abnormal Cells Form Tumours And Interfere With The Normal Functioning Of The Lungs. Since There Are No Apparent Signs Or Symptoms Of Early Lung Cancer, The Clinical Diagnosis Of Lung Cancers Are Often Late, Making Treatment Expensive And Ineffective. Early Diagnosis Of Cancer Is Critical For Providing Victims With The Best Treatment And The Possibility Of A Revival . Detection Of Lung Cancer In Its Early Stages Using CT Scans Would Help Save Many Lives, But Analyzing The Scans Of The Majority Is An Immeasurable Burden For Radiologists. We Are Using A Deep Convolutional Neural Network (DCNN) And Numerous Preprocessing Techniques To Build Up The Exactness Of The Automated Prediction Of Lung Nodules And Its Malignancy Using CT Scans.
Among Brain Tumors, Gliomas Are The Most Common And Aggressive, Leading To A Very Short Life Expectancy In Their Highest Grade. Thus, Treatment Planning Is A Key Stage To Improve The Quality Of Life Of Oncological Patients. Magnetic Resonance Imaging (MRI) Is A Widely Used Imaging Technique To Assess These Tumors, But The Large Amount Of Data Produced By MRI Prevents Manual Segmentation In A Reasonable Time, Limiting The Use Of Precise Quantitative Measurements In The Clinical Practice. So, Automatic And Reliable Segmentation Methods Are Required; However, The Large Spatial And Structural Variability Among Brain Tumors Make Automatic Segmentation A Challenging Problem. In This Paper, We Propose An Automatic Segmentation Method Based On Convolutional Neural Networks (CNN), Exploring Small 3*3 Kernels. The Use Of Small Kernels Allows Designing A Deeper Architecture, Besides Having A Positive Effect Against Over Fitting, Given The Fewer Number Of Weights In The Network.
Among Brain Tumors, Gliomas Are The Most Common And Aggressive, Leading To A Very Short Life Expectancy In Their Highest Grade. Thus, Treatment Planning Is A Key Stage To Improve The Quality Of Life Of Oncological Patients. Magnetic Resonance Imaging (MRI) Is A Widely Used Imaging Technique To Assess These Tumors, But The Large Amount Of Data Produced By MRI Prevents Manual Segmentation In A Reasonable Time, Limiting The Use Of Precise Quantitative Measurements In The Clinical Practice. So, Automatic And Reliable Segmentation Methods Are Required; However, The Large Spatial And Structural Variability Among Brain Tumors Make Automatic Segmentation A Challenging Problem. In This Paper, We Propose An Automatic Segmentation Method Based On Convolutional Neural Networks (CNN), Exploring Small 3*3 Kernels. The Use Of Small Kernels Allows Designing A Deeper Architecture, Besides Having A Positive Effect Against Over Fitting, Given The Fewer Number Of Weights In The Network.
Cardiovascular Disease Is The Leading Cause Of Death Globally. It Accounts For 17.3 Million Deaths/year, And By 2030, This Is Expected To Rise To 23.6 Million [1]. Electrocardiogram (ECG) Signals Are An Important Source Of Information To The Cardiologist For Cardiovascular Disease Diagnosis. Analysis Of Electrocardiogram (ECG) Signals Is Challenging Due To The Complexity Of Their Signal Morphology. However, Analyzing Or Processing These Signal Records Manually To Discover Heart Diseases Is Time-consuming And Sensitive To Human Errors. To Solve This Issue, An Automatic Diagnostic Classification System Is Required, And Many Studies Have Been Proposed. CSP Is A Statistical Method That Has Been Proven To Be An Effective Feature Extraction Method To Discriminate Between Two Classes. The Aim Of Using CSP In This Work Is To Distinguish Between Normal And Abnormal ECG Signals
Leukaemia Is A Group Of Cancers That Usually Begins In The Marrow And Results In High Numbers Of Abnormal White Blood Cells. Leukemia Cells Are Abnormal Cells That Cannot Do What Normal Blood Cells Do. These White Blood Cells Are Not Fully Developed And Are Called Blasts Or Leukemia Cells. Symptoms May Include Bleeding And Bruising Problems, Feeling Very Tired, And An Increased Risk Of Infections. These Symptoms Occur Due To A Lack Of Normal Blood Cells. Diagnosis Is Typically By Blood Tests Or Bone Marrow Biopsy. In Clinical Bioinformatics They Have Allowed Construction Of Powerful Experimental Cancer Diagnostic Models Based On Gene Expression Data With Thousands Of Variables And As Little As Few Dozen Samples. Moreover, Several Efficient And High-quality Implementations Of SVM Algorithms Facilitate Application Of These Techniques In Practice. The Real Power Of Support Vector Machines Is To Map The Data (implicitly) To A Higher Dimensional Space Via A Kernel Function And Then Identify The Maximum-margin Hyper Plane That Separates Training Instances.
The Sclera Is The Opaque And White Outer Layer Of The Eye. The Blood Vessel Structure Of Sclera Is Formed Randomly And Is Unique To Each Person Which Can Be Used For Human’s Identification. Several Researchers Have Designed Different Sclera Vein Recognition Methods And Have Shown That It Is Promising To Use Sclera Vein Recognition For Human Identification. In This Paper, We Proposed A New Parallel Sclera Vein Recognition Method, Which Employees A Two Stage Parallel Approach For Registration And Matching. Even Though The Research Focused On Developing A Parallel Sclera Matching Solution For The Sequential Line-descriptor Method Using CUDA GPU Architecture, The Parallel Strategies Developed In This Research Can Be Applied To Design Parallel Solutions To Other Sclera Vein Recognition Methods And General Pattern Recognition Methods. The Proposed Method Will Dramatically Improve The Matching Efficiency Without Compromising Recognition Accuracy.
The Diabetic Foot Is One Of The Most Devastating Complications Related To Diabetic. Its Significant Transcendence Is Related To A Higher Incidence And Amputation Percentage As Well As Deaths. Given The Fact That Laboratory Diagnoses Trials Are Both Limited And Expensive, The Most Typical Alternative Is Still Based On The Disease’s Signs And Symptoms. Therefore, The Attending Physician Fills Out A Questionnaire Based On Its Support Instrumental Measurements And Its Own Observation. The Aforementioned Questionnaire Will Provide The Foundation For The Diagnoses That Also Depends On The Criteria And The Consultant’s Experience. This Project Aims To Become The First Link To Optimize The Diabetic’s Foot Evaluation Through The Introduction Of Digital Image Processing Techniques. Because Of The Use Of Advanced Object Segmentation Techniques And A Parameter That Adjusts The System’s Sensibility Until Obtaining The Desired Results It Was Possible To Apply An Algorithm To A Series Of Trial Images Provided Positive Results For Wound And Location Detection.
Leukaemia Is A Group Of Cancers That Usually Begins In The Marrow And Results In High Numbers Of Abnormal White Blood Cells. Leukemia Cells Are Abnormal Cells That Cannot Do What Normal Blood Cells Do. These White Blood Cells Are Not Fully Developed And Are Called Blasts Or Leukemia Cells. Symptoms May Include Bleeding And Bruising Problems, Feeling Very Tired, And An Increased Risk Of Infections. These Symptoms Occur Due To A Lack Of Normal Blood Cells. Diagnosis Is Typically By Blood Tests Or Bone Marrow Biopsy. In Clinical Bioinformatics They Have Allowed Construction Of Powerful Experimental Cancer Diagnostic Models Based On Gene Expression Data With Thousands Of Variables And As Little As Few Dozen Samples. Moreover, Several Efficient And High-quality Implementations Of SVM Algorithms Facilitate Application Of These Techniques In Practice. The Real Power Of Support Vector Machines Is To Map The Data (implicitly) To A Higher Dimensional Space Via A Kernel Function And Then Identify The Maximum-margin Hyper Plane That Separates Training Instances.
A Brain Tumor Takes Up Space Within The Skull And Can Interfere With Normal Brain Activity. It Can Increase Pressure In The Brain, Shift The Brain Or Push It Against The Skull, And/or Invade And Damage Nerves And Healthy Brain Tissue. Since Brain Diseases Are Dynamic And Evolutionary In Nature, Their Detection, Treatment Will Also Progress Based On The Dynamic Nature Of The Disease. Therefore, The Image Processing Technique Must Also Progress In A Direction Of Finding Cancer As Early As Possible. The Screening Techniques In Cancer Detection Must Be Reliable, Robust And Must Have High Level Of Diagnostic Value. For This Purpose, Right From The Image Acquisition To The Detection Of Cancer, An Effective Work Is Defined Enough To Identify The Real Indicators Of Cancer Nodules. For This Purpose, The Work Is About To Present A Regression Model Based On A Decent Sample Size Of Subject’s Brain Images. In This, The Tumor Detection Is Performed Using The Clustered Genetic Approach. The Clustering Is Basically Used To Reduce The Data Size On Which The Process Will Be Performed And The Genetic Will Perform The Error Reduction And The Tumor Area Segmentation.
In Clinical Context, Medical Image Plays The Most Significant Role. Medical Imaging Brings Out Internal Structures Concealed By The Skin And Bones, As Well As To Diagnose And Treat Diseases Like Cancer, Diabetic Retinopathy, Fractures In Bones, Skin Diseases Etc. The Medical Imaging Process Is Different For Different Type Of Diseases. The Image Capturing Process Contributes The Noise In The Medical Image. Henceforth, Captured Images Need To Be Noise Free For Proper Diagnosis Of The Diseases. Digital Image Processing Technology Executes Computer Algorithms To Realize Digital Image Processing Which Implies Digital Data Modification That Improves Quality Of The Image. For Medical Image Information Extraction And Foster Analysis, The Implemented Image Processing Algorithm Maximizes The Clarity And Sharpness Of The Image And Also Interesting Features Details. In This Paper, We Discourse Various Noises That Affect The Medical Images And Also Accompanied By The Denoising Algorithms.
Image Denoising Is An Important Pre-processing Step In Medical Image Analysis. Different Algorithms Have Been Proposed In Past Three Decades With Varying Denoising Performances. More Recently, Having Outperformed All Conventional Methods, Deep Learning Based Models Have Shown A Great Promise. These Methods Are However Limited For Requirement Of Large Training Sample Size And High Computational Costs. In This Paper We Show That Using Small Sample Size, Denoising Autoencoders Constructed Using Convolutional Layers Can Be Used For Efficient Denoising Of Medical Images. Heterogeneous Images Can Be Combined To Boost Sample Size For Increased Denoising Performance. Simplest Of Networks Can Reconstruct Images With Corruption Levels So High That Noise And Signal Are Not Differentiable To Human Eye.Convolutional Autoencoders Are Based On Standard Autoencoder Architecture With Convolutional Encoding And Decoding Layers. Compared To Classic Autoencoders, Convolutional Autoencoders Are Better Suited For Image Processing As They Utilize Full Capability Of Convolutional Neural Networks To Exploit Image Structure.
Tumor Is An Uncontrolled Growth Of Tissue In Any Part Of The Body. The Tumor Is Of Different Types And Has Different Characteristics And Corresponding Different Treatment. If We Find The Tumor In Early Stages, We Can Stop Further Growth Of The Tumor And Proper Treatment Is Undergone For The Tumor. Magnetic Resonance Imaging (MRI) Is The Most Important Techniqu1e, In Discovering The Brain Tumor. This MRI Image Is Visually Examined By The Physician For Detection & Diagnosis Of Brain Tumor. However This Method Of Detection Resists The Accurate Determination Of Stage & Size Of Tumor. To Avoid That, This System Uses Computer Aided Method For Segmentation. There Are Different Types Of Algorithm Were Developed For This Segmentation Purpose But They Have Some Drawback In Detection And Extraction. In This System Fuzzy C-mean Clustering Will Be Used Which Allows The Segmentation Of Tumor Tissue With Accuracy And Reproducibility.
Diabetes Is A Disease Which Occurs When The Pancreas Does Not Secrete Enough Insulin Or The Body Is Unable To Process It Properly. The Diabetic Retinopathy (DR) Is A Complication Of Diabetes That Is Caused By Changes In The Blood Vessels Of Retina. The Symptoms Can Distort Or Blur The Patient’s Vision And Are The Main Causes Of Blindness. It Causes 45% Of The Legal Blindness In Diabetes Patients. This Paper Provides An Automated Diagnosis System For DR Integrated With A User-friendly Interface. The Grading Of The Severity Level Of DR Is Based On Detecting And Analyzing The Early Clinical Signs Associated With The Disease, Such As Microaneurysms (MAs) And Hemorrhages (HAs). Based On The Number And Location Of MAs And HAs, The Severity Will Be Graded Into Four Scales, I.e. Normal, Mild, Moderate, Or Severe
Among The Various Types Of Human Cancers, Breast Cancer Is The Most Common Form, Among The Women. However, Complete Curing Of The Said Disease Is Possible If It Is Detected In Its Early Stage. Early Detection Of Breast Cancer Improves Survival Rate Of Women. Mammography Is Currently The Method Of Choice For Early Detection Of Breast Cancer In Women. However, The Interpretation Of Mammograms Is Largely Based On The Radiologist’s Opinion. The Radiologist’s Performance Increases When They Incorporate Automatic Image Analysis In Their Decision Making Process For Both The Detection And Diagnosis Of Cancer. This Paper Proposes A Novel Approach For The Development Of A Computer Aided Decision System To Automatically Detect Abnormalities In Mammograms. Mathematical Morphology Proves To Be A Useful Tool For The Detection Of Abnormalities In Digital Mammograms. We Proposed A New Algorithm For The Detection Of Abnormalities On Mammograms. Every Suspicious Object Is Marked Using A Binary Image, Which Is Used As A Mask For Object Extraction From The Original Image. The Features Of The Extracted Objects Are Classified Using Naive Bayes Classifier.
Diabetes Is A Disease Which Occurs When The Pancreas Does Not Secrete Enough Insulin Or The Body Is Unable To Process It Properly. The Diabetic Retinopathy (DR) Is A Complication Of Diabetes That Is Caused By Changes In The Blood Vessels Of Retina. The Symptoms Can Distort Or Blur The Patient’s Vision And Are The Main Causes Of Blindness. One Of The Difficulties In This Illness Is That The Patient With Diabetes Mellitus Requires A Continuous Screening For Early Detection. So Far, Numerous Methods Have Been Proposed By Researchers To Automate The Detection Process Of DR In Retinal Fungus Images. In This Paper, We Developed An Alternative Simple Approach To Detect DR. This Method Will Built On The Inverse Segmentation. Background Image Approach Along With Inverse Segmentation Will Be Employed To Measure And Follow Up The Degenerations In Retinal Fundus Images.
Cancer Is One Of The Most Serious And Widespread Disease That Is Responsible For Large Number Of Deaths Every Year. Among All Different Types Of Cancers, Lung Cancer Is The Most Prevalent Cancer Having The Highest Mortality Rate. Although CT Is Preferred Over Other Imaging Modalities, Visual Interpretation Of These CT Scan Images May Be An Error Prone Task And Can Cause Delay In Lung Cancer Detection. Therefore, Image Processing Techniques Are Used Widely In Medical Fields For Early Stage Detection Of Lung Tumor. This Paper Presents An Automated Approach For Detection Of Lung Cancer In CT Scan Images. The Algorithm For Lung Cancer Detection Is Proposed Using Methods Such As Median Filtering For Image Preprocessing Followed By Segmentation Of Lung Region Of Interest Using Mathematical Morphological Operations. Geometrical Features Are Computed From The Extracted Region Of Interest And Used To Classify CT Scan Images Into Normal And Abnormal By Using Support Vector Machine.
Epilepsy Is A Common Chronic Neurological Disorder That Is Characterized By Recurrent Unprovoked Seizures. According To A Report By The World Health Organization (WHO), Approximately 50 Million People Worldwide Have Epilepsy, Making It One Of The Most Common Neurological Diseases Globally. Electroencephalogram (EEG) Signal Is Commonly Used By Clinicians In Diagnosis Of Neurological Disorders Including Epileptic Seizures. In This Method, DD-DWT Is Creatively Introduced For EEG Processing Instead Of The Traditional DWT. Additionally, The Mixed Features Of HE And Fuzzy-En Are Exploited To Characterize The Self-similarity And Complexity Of EEG. The Significant Features Selected By ANOVA Are Fed To Classifiers For Final Decision. It Is Apparently Noticed That The GA-SVM Configured With Less Features Is Found To Achieve The Prominent Classification Performance For Various Combinations.
Depression Is An Important Type Of Mood Disorder With Prominent, Prolonged, And Depressed Moods As The Main Clinical Features. Depression Has Become A Serious Disease That Affects People’s Mental Health. How To Detect It Promptly And Accurately Is A Difficult Task. Electroencephalogram Can Reflect The Spontaneous Biological Potential Signals In The Cerebral Cortex And Is Widely Used In The Prediction And Diagnosis Of Depression. With Electroencephalogram, The Key And Most Difficult Challenge Is To Find The Brain Regions And Frequencies Associated With Depression, Especially Mild Depression. At Present, The Most Commonly Used Method Is The Combination Of Feature Selection And Classification Algorithm For Detection. However, The Classification Accuracy Needs To Be Further Improved. The Differential Evolution Is A Population-based Adaptive Global Optimization Algorithm. Due To Its Fast Convergence And Strong Robustness, This Paper Uses It To Optimize The Extracted Features To Achieve Better Result. Then The K-nearest Neighbor Classification Algorithm Is Used To Classify Patients With Mild Depression And Normal People
A Stroke Occurs When The Blood Supply To Part Of Your Brain Is Interrupted Or Reduced, Preventing Brain Tissue From Getting Oxygen And Nutrients. Brain Cells Begin To Die In Minutes. A Stroke Is A Medical Emergency, And Prompt Treatment Is Crucial. Early Action Can Reduce Brain Damage And Other Complications. Arm Rehabilitation In An Important Process After Stroke To Regain Movement Skill Lost. The Main Goal Of Stroke Rehabilitation Is To Regain Independence And Improve Life’s Quality. The System Presents 18 Fundamental Movements For The Rehabilitation Of The Stroke Patient. The Objective Of This Research Is To Develop The Movement Sequences Which Are Suitable For The Rehabilitation Process And Is Focused On Hemiparesis Sufferers Which Are The Most Common Among Stroke Patients.
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Tuberculosis (TB) Is An Airborne Infectious Disease Caused By Organisms In The Mycobacterium Tuberculosis (Mtb) Complex. In Many Low And Middle-income Countries, TB Remains A Major Cause Of Morbidity And Mortality. Once A Patient Has Been Diagnosed With TB, It Is Critical That Healthcare Workers Make The Most Appropriate Treatment Decision Given The Individual Conditions Of The Patient And The Likely Course Of The Disease Based On Medical Experience. Depending On The Prognosis, Delayed Or Inappropriate Treatment Can Result In Unsatisfactory Results Including The Exacerbation Of Clinical Symptoms, Poor Quality Of Life, And Increased Risk Of Death. The Advent Of Digital Chest Radiography And The Possibility Of Digital Image Processing Has Given New Impetus To Computeraided Screening And Diagnosis. This System Present An Automated Approach For Detecting TB Manifestations In Chest X-rays (CXRs), Based Earlier Work In Lung Segmentation And Lung Disease Classification. In An Effort To Reduce The Burden Of The Disease, Automated Approach For Detecting Tuberculosis In Conventional Posteroanterior Chest Radiographs
Prostate Cancer Is One Of The Major Causes Of Cancer Death For Men In The Western World. Magnetic Resonance Imaging (MRI) Is Being Increasingly Used As A Modality To Detect Prostate Cancer. Therefore, Computer-aided Detection Of Prostate Cancer In MRI Images Has Become An Active Area Of Research. In This Paper We Investigate A Fully Automated Computer-aided Detection System Which Consists Of Two Stages. In The First Stage, We Detect Initial Candidates Using Multi-atlas-based Prostate Segmentation, Voxel Feature Extraction, Classification And Local Maxima Detection. The Second Stage Segments The Candidate Regions And Using Classification We Obtain Cancer Likelihoods For Each Candidate. Features Represent Pharmacokinetic Behavior, Symmetry And Appearance, Among Others. Furthermore, The System Has Potential In A First-reader Setting.
One Of The Most Rapidly Spreading Cancers Among Various Other Types Of Cancers Known To Humans Is Skin Cancer. Melanoma Is The Worst And The Most Dangerous Type Of Skin Cancer That Appears Usually On The Skin Surface And Then Extends Deeper Into The Layers Of Skin. However, If Diagnosed At An Early Stage; The Survival Rate Of Melanoma Patients Is 96% With Simple And Economical Treatments. The National Cancer Institute Estimated That The Cost Of Melanoma Was $2.36 Billion In 2010 And Will Continue To Increase In Upcoming Years. The Conventional Method Of Diagnosing Melanoma Involves Expert Dermatologists, Equipment, And Biopsies. To Avoid The Expensive Diagnosis, And To Assist Dermatologists, The Field Of Machine Learning Has Proven To Provide State Of The Art Solutions For Skin Cancer Detection At An Earlier Stage With High Accuracy.
Tumor Is An Uncontrolled Growth Of Tissue In Any Part Of The Body. The Tumor Is Of Different Types And Has Different Characteristics And Corresponding Different Treatment. If We Find The Tumor In Early Stages, We Can Stop Further Growth Of The Tumor And Proper Treatment Is Undergone For The Tumor. Magnetic Resonance Imaging (MRI) Is The Most Important Techniqu1e, In Discovering The Brain Tumor. This MRI Image Is Visually Examined By The Physician For Detection & Diagnosis Of Brain Tumor. However This Method Of Detection Resists The Accurate Determination Of Stage & Size Of Tumor. To Avoid That, This System Uses Computer Aided Method For Segmentation. There Are Different Types Of Algorithm Were Developed For This Segmentation Purpose But They Have Some Drawback In Detection And Extraction. The Objective Of This Paper Is To Develop An Enhanced K-means And Kernelized Fuzzy C-means For A Segmentation Of Brain Magnetic Resonance Images.
Tumor Is An Uncontrolled Growth Of Tissue In Any Part Of The Body. The Tumor Is Of Different Types And Has Different Characteristics And Corresponding Different Treatment. If We Find The Tumor In Early Stages, We Can Stop Further Growth Of The Tumor And Proper Treatment Is Undergone For The Tumor. Magnetic Resonance Imaging (MRI) Is The Most Important Techniqu1e, In Discovering The Brain Tumor. This MRI Image Is Visually Examined By The Physician For Detection & Diagnosis Of Brain Tumor. Due To Complex Appearance In MRI, Brain Tumor Texture Is Formulated Using A Multiresolutionfractal Model Known As Multifractional Brownian Motion (mBm). Detailed Mathematical Derivation For MBm Model And Corresponding Novel Algorithm To Extract Spatially Varying Multifractal Features Are Proposed.
Tuberculosis (TB) Is An Airborne Infectious Disease Caused By Organisms In The Mycobacterium Tuberculosis (Mtb) Complex. In Many Low And Middle-income Countries, TB Remains A Major Cause Of Morbidity And Mortality. Once A Patient Has Been Diagnosed With TB, It Is Critical That Healthcare Workers Make The Most Appropriate Treatment Decision Given The Individual Conditions Of The Patient And The Likely Course Of The Disease Based On Medical Experience. Depending On The Prognosis, Delayed Or Inappropriate Treatment Can Result In Unsatisfactory Results Including The Exacerbation Of Clinical Symptoms, Poor Quality Of Life, And Increased Risk Of Death.The Advent Of Digital Chest Radiography And The Possibility Of Digital Image Processing Has Given New Impetus To Computeraided Screening And Diagnosis. This System Present An Automated Approach For Detecting TB Manifestations In Chest X-rays (CXRs), Based Earlier Work In Lung Segmentation And Lung Disease Classification. In An Effort To Reduce The Burden Of The Disease, Automated Approach For Detecting Tuberculosis In Conventional Posteroanterior Chest Radiographs.
An Electrocardiogram (ECG) Plays An Important Role In The Diagnosis Process And Providing Information Regarding Heart Diseases, Monitoring ECG Signal Has High Clinical Significance. Out Of 125 Babies, 1 Baby Born With Some Form Of Congenital Heart Defect Every Year. So Analysis And Synthesis Of Fetal Electrocardiograms (FECG) For Disease Detection Is Very Important. The FECG Is Always Contaminated By Mother’s ECG (MECG) So, Extracting The Clean FECG Signal Is Very Necessary For Fetal Health Monitoring. Currently, There Is An Intense Research Into Noninvasive Methods For Detecting The Fetus At Risk Of Damage Or Death In The Uterus. The Fetal Electrocardiogram (FECG) Provides Useful Information About The Fetus’s Condition. However, ICA Is Computationally Demanding Due To Its Use Of Higher-order Statistics, And Hence, It Is Not Well Suited For Implementation In Real-time Applications. To Cope With This Problem, Traditional Approaches Have Been Based On Adaptive Algorithms Or, More Recently, On The Use Of A Priori Information On The ECG Signal.
With The Rapid Development Of Telediagnosis, Telesurgery As Well As Hospital Information System, Medical Images Have Become One Of The Most Important Tools In Helping Physicians To Determine Suitable Diagnostic Procedures. Currently, Most Medical Images Are Stored And Exchanged With Little Or No Security; Hence It Is Important To Provide Protection For The Intellectual Property Of These Images In A Secured Environment. In This Paper, A New And Reversible Watermarking Method Is Proposed To Address This Security Issue. Specifically, Signature Information And Textual Data Are Inserted Into The Original Medical Images Based On Recursive Dither Modulation (RDM) Algorithm After Wavelet Transform And Singular Value Decomposition (SVD). In Addition, Differential Evolution (DE) Is Applied To Design The Quantization Steps (QSs) Optimally For Controlling The Strength Of The Watermark. Using These Specially Designed Hybrid Techniques, The Proposed Watermarking Technique Obtains Good Imperceptibility And High Robustness. Experimental Results Indicate That The Proposed Method Is Not Only Highly Competitive, But Also Outperforms The Existing Methods.
Biomedical Image Analysis Plays A Critical Role In Life Sciences And Health Care. In Particular, Robust And Efficient Enhancement, Segmentation, Analysis, And Modeling Of Curve-like Structures Is A Very Common Requirement In Bioimage Informatics. Many Biomedical Applications Require Detection Of Curvilinear Structures In Images And Would Benefit From Automatic Or Semiautomatic Segmentation To Allow High-throughput Measurements. Here, We Propose A Contrast-independent Approach To Identify Curvilinear Structures Based On Oriented Phase Congruency, I.e., The Phase Congruency Tensor (PCT). We Show That The Proposed Method Is Largely Insensitive To Intensity Variations Along The Curve And Provides Successful Detection Within Noisy Regions. The Performance Of The PCT Is Evaluated By Comparing It With State-of-the-art Intensity-based Approaches On Both Synthetic And Real Biological Images.
Diabetes Is A Disease Which Occurs When The Pancreas Does Not Secrete Enough Insulin Or The Body Is Unable To Process It Properly. The Diabetic Retinopathy (DR) Is A Complication Of Diabetes That Is Caused By Changes In The Blood Vessels Of Retina. The Symptoms Can Distort Or Blur The Patient’s Vision And Are The Main Causes Of Blindness. One Of The Difficulties In This Illness Is That The Patient With Diabetes Mellitus Requires A Continuous Screening For Early Detection. We Propose An Algorithm For The Detection Of Retinal Landmarks (optic Nerve Head Or Optic Disc, Macula, And Vasculature) Based On Optic Cup Location And Anatomical Structural Details From Diabetic Retinopathy (DR) Images Of Both Left And Right Eye. Our Algorithm Uses Color Fundus Images Obtained From Mydriatic Camera. This System Intends To Help The Ophthalmologists Not Only In DR Screening Process But Any Other Eye Related Abnormality Which Is Based On Retinal Photography.
Depression Is An Important Type Of Mood Disorder With Prominent, Prolonged, And Depressed Moods As The Main Clinical Features. Depression Has Become A Serious Disease That Affects People’s Mental Health. How To Detect It Promptly And Accurately Is A Difficult Task. Electroencephalogram Can Reflect The Spontaneous Biological Potential Signals In The Cerebral Cortex And Is Widely Used In The Prediction And Diagnosis Of Depression. With Electroencephalogram, The Key And Most Difficult Challenge Is To Find The Brain Regions And Frequencies Associated With Depression. The Linear And Nonlinear Methods Are Effective In Identifying The Changes In EEG Signals For The Detection Of Depression. Linear Methods Do Not Exhibit The Complex Dynamical Variations In The EEG Signals. Hence, Chaos Theory And Nonlinear Dynamic Methods Are Widely Used In Extracting The EEG Signal Features For Computer-aided Diagnosis (CAD) Of Depression.
Image Processing Has Several Applications In Areas Such As Medicine, Industry, Agriculture, Etc. Most Of The Methods Of Image Processing Require A First Step Called Segmentation. Segmentation Is One Of The Most Important Tasks In Image Processing. It Consists Of Classifying The Pixels Into Two Or More Groups Depending On Their Intensity Levels And A Threshold Value. The Quality Of The Segmentation Depends On The Method Applied To Select The Threshold. When The Image Is Segmented Into Two Classes, The Task Is Called Bi-level Thresholding (BT) And It Requires Only One Th Value. On The Other Hand, When Pixels Are Separated Into More Than Two Classes, The Task Is Named As Multilevel Thresholding (MT) And Demands More Than One Th Value. The Use Of The Classical Implementations For Multilevel Thresholding Is Computationally Expensive Since They Exhaustively Search The Best Values To Optimize The Objective Function. Under Such Conditions, The Use Of Optimization Evolutionary Approaches Has Been Extended. The Electromagnetism-like Algorithm (EMO) Is An Evolutionary Method Which Mimics The Attraction–repulsion Mechanism Among Charges To Evolve The Members Of A Population. Different To Other Algorithms, EMO Exhibits Interesting Search Capabilities Whereas Maintains A Low Computational Overhead. In This Paper, A Multilevel Thresholding (MT) Algorithm Based On The EMO Is Introduced
Diabetes Is A Disease Which Occurs When The Pancreas Does Not Secrete Enough Insulin Or The Body Is Unable To Process It Properly. The Diabetic Retinopathy (DR) Is A Complication Of Diabetes That Is Caused By Changes In The Blood Vessels Of Retina. The Symptoms Can Distort Or Blur The Patient’s Vision And Are The Main Causes Of Blindness. One Of The Difficulties In This Illness Is That The Patient With Diabetes Mellitus Requires A Continuous Screening For Early Detection. So Far, Numerous Methods Have Been Proposed By Researchers To Automate The Detection Process Of DR In Retinal Fungus Images. In This Paper, We Developed An Alternative Simple Approach To Detect DR. This Method Will Built On The Inverse Segmentation. Background Image Approach Along With Inverse Segmentation Will Be Employed To Measure And Follow Up The Degenerations In Retinal Fundus Images.
Brain Hemorrhage Is A Type Of Stroke Which Is Caused By An Artery In The Brain Bursting And Causing Bleeding In The Surrounded Tissues. Diagnosing Brain Hemorrhage, Which Is Mainly Through The Examination Of A CT Scan Enables The Accurate Prediction Of Disease And The Extraction Of Reliable And Robust Measurement For Patients In Order To Describe The Morphological Changes In The Brain As The Recovery Progresses. Though A Lot Of Research On Medical Image Processing Has Been Done, Still There Is Opportunity For Further Research In The Area Of Brain Hemorrhage Diagnosis Due To The Low Accuracy Level In The Current Methods And Algorithms, Coding Complexity Of The Developed Approaches, Impracticability In The Real Environment, And Lack Of Other Enhancements Which May Make The System More Interactive And Useful. This Proposed Method Investigates The Possibility Of Diagnosing Brain Hemorrhage Using An Image Segmentation Of CT Scan Images Using Watershed Method And Feeding Of The Appropriate Inputs Extracted From The Brain CT Image To An Artificial Neural Network For Classification.
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