Automated Detection of B Cell and T Cell Acute Lymphoblastic Leukaemia Using Deep Learning

Volume 43, Issue 5, October 2022, Pages 405-413

Highlights•

Deep Learning based classification of ALL.

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Classification of ALL based on WHO scheme.

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ALL is classified into B – cell lymphoblasts and T – cell lymphoblasts.

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Use of Convolutional Neural Networks (CNN) for leukaemia classification.

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Leukaemia classification without image segmentation and feature extraction.

AbstractPurpose

Leukaemia is diagnosed conventionally by observing the peripheral blood and bone marrow smear using a microscope and with the help of advanced laboratory tests. Image processing-based methods, which are simple, fast, and cheap, can be used to detect and classify leukemic cells by processing and analysing images of microscopic smear. The proposed study aims to classify Acute Lymphoblastic Leukaemia (ALL) by Deep Learning (DL) based techniques.

Procedures

The study used Deep Convolutional Neural Networks (DNNs) to classify ALL according to WHO classification scheme without using any image segmentation and feature extraction that involves intense computations. Images from an online image bank of American Society of Haematology (ASH) were used for the classification.

Findings

A classification accuracy of 94.12% is achieved by the study in isolating the B-cell and T-cell ALL images using a pretrained CNN AlexNet as well as LeukNet, a custom-made deep learning network designed by the proposed work. The study also compared the classification performances using three different training algorithms.

Conclusions

The paper detailed the use of DNNs to classify ALL, without using any image segmentation and feature extraction techniques. Classification of ALL into subtypes according to the WHO classification scheme using image processing techniques is not available in literature to the best of the knowledge of the authors. The present study considered the classification of ALL only, and detection of other types of leukemic images can be attempted in future research.

Graphical abstract