Acute Leukemia Detection using Deep Learning Techniques

Abstract

Leukocytes, which are created in the bone marrow, comprise one percent of all blood cells. When these white blood cells grow uncontrollably, it gives rise to the development of blood cancer. The proposed research presents an approach for categorizing one of the three kinds of malignancy known as acute lymphoblastic leukaemia (ALL). To start with, in ALL, an excessively large number of lymphocytes are produced by the bone marrow. Secondly, Multiple myeloma (MM) is a type of cancer that results in the accumulation of malignant cells in the bone marrow, rather than their release into the bloodstream. Hence, the growth of blood cells is to be resisted and prevented. Previously, the procedure was carried out manually, evaluated by experienced haematologists. The proposed methodology totally eliminates the chance of human mistake through using deep learning methods, particularly convolutional neural networks. A total of 89 ALL patients' 3256 smears of peripheral blood (PBS) pictures were acquired from an online portal. The model undergoes training using modified convolutional neural networks that have been optimized, and its ability to predict which type of malignancy is present in the cells is determined. In 96 out of 100 cases, the algorithm strongly replicated every measurement that corresponded to the samples. The accuracy of the system was found to be 97.6%, which is more appropriate than modern techniques like Decision Trees, Random Forests, Naive Bayes, and Support Vector Machines (SVMs), VGG16, VGG19, AlexNet, GoogleNet, MobileNetV2. The work showcases that Modified CNN performs more accurately.