Identification and Classification of Acute Lymphocytic Leukemia Using Weighted Ensemble Neural Networks
Hema Patel · Journal of Information Systems Engineering & Management · 2025
Hematologic malignancy puts the lives of numerous people of all the ages at serious risk globally. Acute lymphoblastic leukemia (ALL), a sub type of leukemia, is a dreadful disease that affects millions of individual globally. Early detection is crucial, and image analysis of peripheral blood smears is a simple way to diagnose and categorize leukemic cells. However, image analysis is complex and can lead to incorrect findings. To address this, a model is proposed to examine ALL and identify its various categories using deep ensemble learning. This study shows that a deep learning method based on an ensemble weight threshold improves leukemia detection using imbalanced dataset which reflects the real-life scenario. The proposed model examines healthy and ALL cells and identify its various categories, including Benign, Pro-B, Early Pre-B, and Pre-B from peripheral blood smear images to help in the early detection of this life-threatening disease with the highest accuracy (99.96%), precision (99.86%), sensitivity (99.93%), specificity (99.97%), F1-Score (99.89%). The model also outperforms five other pre-trained networks, viz. AlexNet, VGG-16, DenseNet-210, ResNet-50, and MobileNetV2 in classification accuracy, demonstrating its potential as an effective early diagnostic tool for ALL and its subtypes