Classification of acute lymphoblastic leukaemia using machine learning algorithms

Acute Lymphoblastic Leukemia is a serious disease which may cause death if it is not detected at an early stage. It is common in children as well as adults. The detection of ALL is being done manually by examining the blood smear samples under a microscope. Manual blood testing has got several shortcomings such as it is slow and accuracy is also less. Generally, the inspection is done by an experienced pathologists and if there is any malformations the presence of lymphoblasts will be there. The accuracy of the diagnosis will be dependent on the experience of the operator.  The Proposed research work aims in improving the diagnosis of ALL using Machine Learning Classifiers.   Few classifiers haven been applied and compared on the segmented dataset images. The automated system can provide several advantages like it will minimize human intervention and it would provide more accurate results. In this research work EDES-SVM and EDSC-SVM have been used for classification.  Experimental results obtained are then compared with the results of other machine learning classifiers such as SVM, ESVM, DSC-SVM, DES-SVM. From the experimental results it is analysed that the proposed method outperforms the conventional methods.&nbsp