An Intelligent Medical Imaging Approach for Various Blood Structure Classifications

Blood is a vital body fluid and can be instrumental in identifying various pathological conditions. Nowadays, lot of people are suffering from COVID-19 every country has its own limited testing capacity. Consequently, system required to help doctors analyze patient’s blood structure including COVID-19. Therefore, this paper, we extracted selected features by proposing new feature extraction selection method named stepwise linear discriminant analysis (SWLDA). SWLDA emphasizes on picking confined images discerning class based reversion value such as partial F value. begins with an equivalence comprising the sole finest X variable then puts effort add more Xs individually, providing situations adequate. The process adding determine which would entered. Then, picked or default F-to-enter compared uppermost After step, forward addition backward removal whether test values for all predictor variables already line estimated known. comparison made between lowermost (FL) preselected defaulting consequence levels F0 (i.e., if > FL, ZL removed, started again; otherwise, regression equation adopted). Finally, trained employing support vector machine (SVM) label images. performance proposed approach assessed 8 different datasets structures. It assured that achieved significant results under