Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks

Version 1 : Received: 17 May 2021 / Approved: 19 May 2021 / Online: 19 May 2021 (07:42:23 CEST)

How to cite: Mondal, C.; Hasan, M.K.; Jawad, M.T.; Dutta, A.; Islam, M.R.; Awal, M.A.; Ahmad, M.; Alyami, S.A.; Ali Moni, M. Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks. Preprints 2021, 2021050429 (doi: 10.20944/preprints202105.0429.v1). Mondal, C.; Hasan, M.K.; Jawad, M.T.; Dutta, A.; Islam, M.R.; Awal, M.A.; Ahmad, M.; Alyami, S.A.; Ali Moni, M. Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks. Preprints 2021, 2021050429 (doi: 10.20944/preprints202105.0429.v1).

Abstract

Although automated Acute Lymphoblastic Leukemia (ALL) detection is essential, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy is arduous, time-consuming, often suffers inter-observer variations, and necessitates experienced pathologists. This article has automated the ALL detection task, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of deep CNNs to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates' corresponding metrics, such as accuracy, F1-score, AUC, and kappa values. Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. We train and evaluate the proposed model utilizing the publicly available C-NMC-2019 ALL dataset. Our proposed weighted ensemble model has outputted a weighted F1-score of 88.6%, a balanced accuracy of 86.2%, and an AUC of 0.941 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately produce coarse and scatter learned areas for most example cases. Since the proposed ensemble yields a better result for the aimed task, it can experiment in other domains of medical diagnostic applications.

Subject Areas

Acute lymphoblastic leukemia; Deep convolutional neural networks; Ensemble image classifiers; C-NMC-2019 dataset.

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