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.
