Detection and Classification of Acute Lymphoblastic Leukemia Utilizing Deep Transfer Learning

TL;DR

This paper presents a deep learning approach using CNNs, specifically MobileNetV2 and a custom model, to accurately classify stages of leukemia from blood cell images, aiding early diagnosis.

Contribution

It introduces a novel application of transfer learning and custom CNN architectures for leukemia stage classification with high accuracy.

Findings

MobileNetV2 achieved 99.69% accuracy.

Custom CNN model achieved 98.6% accuracy.

Synthetic data augmentation improved model performance.

Abstract

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.