Published Paper Details:

Acute Lymphoblastic Leukemia (ALL) is the most widely recognized malignant growth in youngsters and grown- ups. The identification of leukemia in the prior stage is significant before it spreads into the circulatory systems and other fundamental organs. For a considerable length of time, the finding of leukemia has been finished by experienced administrators and it is a tedious undertaking for pathologists. Programmed recognition of Acute lymphoblastic leukemia disease in tiny pictures is difficult because of the complicated structures. To handle this issue, a programmed and powerful indicative framework is required for early identification and treatment. Right now, machine learning-based implementation of Transfer learning for classification is proposed to recognize leukemic cells and ordinary cells. The purpose of this paper is to compare three types of Convolution Neural Network (CNN) architectures and to find which of them is suitable for our detection application. These architectures are MobileNet, VGG16 and ResNet50. All the methods are done using Google Colab which is a free cloud service to create machine learning model, Tensorflow a machine learning library, openCV, keras and other libraries to accurately detect Acute Lymphoblastic Leukemia.