A Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification

TL;DR

This paper presents a hybrid deep learning approach with data augmentation for accurate classification of leukemic B-lymphoblasts in microscopic images, achieving high accuracy and improved performance over individual models.

Contribution

It introduces a novel hybrid deep learning architecture combined with data augmentation techniques for improved leukemic cell classification.

Findings

Achieved 96.17% overall accuracy

Attained 95.17% sensitivity

Reached 98.58% specificity

Abstract

Automatic detection of leukemic B-lymphoblast cancer in microscopic images is very challenging due to the complicated nature of histopathological structures. To tackle this issue, an automatic and robust diagnostic system is required for early detection and treatment. In this paper, an automated deep learning-based method is proposed to distinguish between immature leukemic blasts and normal cells. The proposed deep learning based hybrid method, which is enriched by different data augmentation techniques, is able to extract high-level features from input images. Results demonstrate that the proposed model yields better prediction than individual models for Leukemic B-lymphoblast classification with 96.17% overall accuracy, 95.17% sensitivity and 98.58% specificity. Fusing the features extracted from intermediate layers, our approach has the potential to improve the overall classification performance.