During lab tests, thousands of medical images are generated to trace the
disease's symptoms. Manual interpretation of this data may consume excessive time
and thus may delay diagnosis. Timely detection of critical diseases is very important as
their stage can be changed over an interval. Automated analysis of medical data can
reduce the gap between disease detection and its diagnosis and it also reduces the
overall computational cost. In this paper, this goal will be achieved using different
methods (Classification/ Segmentation/ Image Encoding/ Decoding/ Registration/
Restoration/ Morphology).
Keywords: Disease, Diagnosis, Healthcare, Medical image analysis, Prediction.
