Deep Learning with Self-Attention and Enhanced Preprocessing for Precise Diagnosis of Acute Lymphoblastic Leukemia from Bone Marrow Smears in Hemato-Oncology

TL;DR

This paper introduces a deep learning framework combining enhanced preprocessing, a VGG19 with multi-head self-attention, and Focal Loss to achieve highly accurate and efficient automated diagnosis of acute lymphoblastic leukemia from bone marrow smear images.

Contribution

It presents a novel deep learning approach integrating self-attention and advanced preprocessing for improved leukemia diagnosis accuracy.

Findings

Achieved 99.25% accuracy in ALL detection

Outperformed ResNet101 baseline in accuracy

Enhanced model efficiency and robustness

Abstract

Acute lymphoblastic leukemia (ALL) is a prevalent hematological malignancy in both pediatric and adult populations. Early and accurate detection with precise subtyping is essential for guiding therapy. Conventional workflows are complex, time-consuming, and prone to human error. We present a deep learning framework for automated ALL diagnosis from bone marrow smear images. The method combines a robust preprocessing pipeline with convolutional neural networks (CNNs) to standardize image quality and improve inference efficiency. As a key design, we insert a multi-head self-attention (MHSA) block into a VGG19 backbone to model long-range dependencies and contextual relationships among cellular features. To mitigate class imbalance, we train with Focal Loss. Across evaluated architectures, the enhanced VGG19+MHSA trained with Focal Loss achieves 99.25% accuracy, surpassing a strong ResNet101 baseline (98.62%). These results indicate that attention-augmented CNNs, coupled with targeted loss optimization and preprocessing, yield more discriminative representations of leukemic cell morphology. Our approach offers a highly accurate and computationally efficient tool for automated ALL recognition and subtyping, with potential to accelerate diagnostic workflows and support reliable decision-making in clinical settings.