LeukoNet: DCT-based CNN architecture for the classification of normal versus Leukemic blasts in B-ALL Cancer

TL;DR

LeukoNet is a novel deep learning model that combines DCT domain features and optical density features to accurately distinguish between leukemic blasts and normal cells in microscopic images, aiding leukemia diagnosis.

Contribution

This paper introduces LeukoNet, a CNN architecture that integrates DCT-based features with optical density features for improved cell classification.

Findings

High accuracy in classifying leukemic vs. normal cells

Robust performance validated through extensive experiments

Effective feature fusion enhances classification robustness

Abstract

Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar. In this paper, we propose a deep learning framework for classifying immature leukemic blasts and normal cells. The proposed model combines the Discrete Cosine Transform (DCT) domain features extracted via CNN with the Optical Density (OD) space features to build a robust classifier. Elaborate experiments have been conducted to validate the proposed LeukoNet classifier.