Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning
This work addresses the arduous and time-consuming diagnosis of leukemia, which is treatable if caught early, but it is incremental as it applies existing deep learning methods to a medical dataset.
The study tackled the problem of diagnosing acute lymphoblastic leukemia across four stages using deep learning, achieving accuracies of 98.6% with a custom CNN model and 99.69% with MobileNetV2.
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.