Optimizing Deep Learning Framework for Effective Histopathological Leukemia Detection and Classification: A Hierarchical Approach - M., Kalaiyarasi; Rajaguru, Harikumar; S., Karthikeyan (Journal of Innovative Image Processing, 2025)
For patients with acute lymphoblastic leukemia (ALL), one of the main causes of cancer-related mortality, a timely and precise diagnosis is essential for improving their prognosis. To achieve this, this paper presents a sequential deep learning method for the classification of ALL based on the histopathological diagnosis of PBS images. The publicly accessible Kaggle dataset was used to extract image samples from 3256 benign patients and three types of malignancy (Initial Pre-B, Intermediate Pre-B, and Advanced Pro-B). Using data augmentation techniques, the database's size was increased to 6,512 photos to make the model more broadly applicable. After individual training and evaluation, the five pre-trained deep learning models—InceptionNetV3, EfficientNetB0, VGG19, ResNet50, and DenseNet201—achieved accuracy rates of 93.2%, 92.5%, 91.8%, 90.3%, and 89.7%, respectively. The models' overall accuracy for a hierarchical class was evaluated at an astounding 98.15%. The performance evaluation indicates that the model is adjustable with an MCC of 0.973 and a Kappa of 0.97. In clinical use, the new approach significantly decreased the misclassification rate and outperformed the single models, indicating that it may be a dependable and effective diagnostic method for early detection of leukemia.
Keywords: Acute Lymphoblastic Leukemia (ALL); Data Augmentation; Sequential Layered Framework; Deep Learning; Histopathological Images; Peripheral Blood Smear (PBS); Hierarchical Model