A mutation in the DNA of a single cell that compromises its function initiates leukemia,leading to the overproduction of immature white blood cells that encroach upon the space required for the generation of healthy blood cells.Leukemia is treatable if identified in its initial stages. However,its diagnosis is both arduous and time consuming. This study proposes a novel approach for diagnosing leukemia across four stages Benign,Early,Pre,and Pro using deep learning techniques.We employed two Convolutional Neural Network (CNN) models as MobileNetV2 with an altered head and a custom model. The custom model consists of multiple convolutional layers,each paired with corresponding max pooling layers.We utilized MobileNetV2 with ImageNet weights,adjusting the head to integrate the final results.The dataset used is the publicly available "Acute Lymphoblastic Leukemia (ALL) Image Dataset", and we applied the Synthetic Minority Oversampling Technique (SMOTE) to augment and balance the training dataset.The custom model achieved an accuracy of 98.6%, while MobileNetV2 attained a superior accuracy of 99.69%. The pretrained model showed promising results,indicating an increased likelihood of real-world application.

An estimated 300,000 new cases of leukemia are diagnosed each year which is 2.8 percent of all new cancer cases and the prevalence is rising day by day. The most dangerous and deadly type of leukemia is acute lymphoblastic leukemia (ALL), which affects people of all age groups, including children and adults. In this study, we propose an automated system to detect various-shaped ALL blast cells from microscopic blood smears images using Deep Neural Networks (DNN). The system can detect multiple subtypes of ALL cells with an accuracy of 98 percent. Moreover, we have developed a telediagnosis software to provide real-time support to diagnose ALL subtypes from microscopic blood smears images.