Toward Automated Classification of B-Acute Lymphoblastic Leukemia

Kulhalli, Rahul; Savadikar, Chinmay; Garware, Bhushan

doi:10.1007/978-981-15-0798-4_7

Cited by 12 publications

(4 citation statements)

References 15 publications

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“…Studies using the ensemble technique to classify B-ALL images from normal precursor blasts have yielded better results. One study assembled SENet and PNASNet-5 including ResNet, VGG, DenseNet, Inception V3, DenseNet, and IncptionReseNetV3 as three pretrained networks employed in an ensemble model [36], and also in another research, ResNeXt50 and ResNeXt101 were assembled to classify images [37]. Investigation on research that deals with C-NMC images can be concluded that ensemble learning can significantly improve the generalization ability of the learning system, thereby enhancing the performance of the available methods.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Ghaderzadeh

Hosseini

Asadi

et al. 2022

Scientific Programming

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Introduction. Acute lymphoblastic leukemia (ALL) is the most common type of leukemia, a deadly white blood cell disease that impacts the human bone marrow. ALL detection in its early stages has always been riddled with complexity and difficulty. Peripheral blood smear (PBS) examination, a common method applied at the outset of ALL diagnosis, is a time-consuming and tedious process that largely depends on the specialist’s experience. Materials and Methods. Herein, a fast, efficient, and comprehensive model based on deep learning (DL) was proposed by implementing eight well-known convolutional neural network (CNN) models for feature extraction on all images and classification of B-ALL lymphoblast and normal cells. After evaluating their performance, four best-performing CNN models were selected to compose an ensemble classifier by combining each classifier’s pretrained model capabilities. Results. Due to the close similarity of the nuclei of cancerous and normal cells, CNN models alone had low sensitivity and poor performance in diagnosing these two classes. The proposed model based on the majority voting technique was adopted to combine the CNN models. The resulting model achieved a sensitivity of 99.4, specificity of 96.7, AUC of 98.3, and accuracy of 98.5. Conclusion. In classifying cancerous blood cells from normal cells, the proposed method can achieve high accuracy without the operator’s intervention in cell feature determination. It can thus be recommended as an extraordinary tool for the analysis of blood samples in digital laboratory equipment to assist laboratory specialists.

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Section: Discussionmentioning

confidence: 99%

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Ghaderzadeh

Hosseini

Asadi

et al. 2022

Scientific Programming

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“…(Refer to the submission on the leaderboard (Gupta et al, 2019) with the username: shubham14100). Various recent research works discussed in Sec-1 used superior CNN architectures, say Resnet, ResNext, Densenet (Kulhalli et al, 2019) and ensemble of these architectures (Ding et al, 2019) using crossentropy loss to evaluate weighted F 1 score on CNMC-2019 dataset. However, our weighted F 1 score is so far highest on this dataset as reported in Table-3 because we have addressed the fundamental issue of heterogeneity in data by handling intersubject and intrasubject variability (subject-level heterogeneity) via incorporating it in sampling and the heterogeneity loss function (L H ).…”

Section: Discussionmentioning

confidence: 99%

Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer

Goswami¹,

Mehta²,

Sahrawat³

et al. 2020

Preprint

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Developing nations lack adequate number of hospitals with modern equipment and skilled doctors. Hence, a significant proportion of these nations' population, particularly in rural areas, is not able to avail specialized and timely healthcare facilities. In recent years, deep learning (DL) models, a class of artificial intelligence (AI) methods, have shown impressive results in medical domain. These AI methods can provide immense support to developing nations as affordable healthcare solutions. This work is focused on one such application of blood cancer diagnosis. However, there are some challenges to DL models in cancer research because of the unavailability of a large data for adequate training and the difficulty of capturing heterogeneity in data at different levels ranging from acquisition characteristics, session, to subject-level (within subjects and across subjects). These challenges render DL models prone to overfitting and hence, models lack generalization on prospective subjects' data. In this work, we address these problems in the application of B-cell Acute Lymphoblastic Leukemia (B-ALL) diagnosis using deep learning. We propose heterogeneity loss that captures subject-level heterogeneity, thereby, forcing the neural network to learn subject-independent features. We also propose an unorthodox ensemble strategy that helps us in providing improved classification over models trained on 7-folds giving a weighted-F 1 score of 95.26% on unseen (test) subjects' data that are, so far, the best results on the C-NMC 2019 dataset for B-ALL classification.

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“…Prellberg and Kramer [60] conferred a leukemia cell classification model using ResNeXt [84] model with Squeeze-and-Excitation modules [34]. The authors in [46] produced an automated stain-normalized white blood cell classifier that can classify a malignant (B-ALL) cell or a healthy (HEM) cell. They used the…”

Section: Literature Reviewmentioning

confidence: 99%

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

Mondal¹,

Hasan²,

Jawad³

et al. 2021

Preprint

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Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by numerous immature lymphocytes. Even though automation in ALL prognosis is an essential aspect of cancer diagnosis, it is challenging due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands experienced pathologists to carefully read the cell images, which is arduous, time-consuming, and often suffers inter-observer variations. This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs). We explore the weighted ensemble of different deep CNNs to recommend a better ALL cell classifier.The weights for the ensemble candidate models are estimated from their 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 utilize the publicly available C-NMC-2019 ALL dataset to conduct all the comprehensive experiments. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, 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, 1

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Toward Automated Classification of B-Acute Lymphoblastic Leukemia

Cited by 12 publications

References 15 publications

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

Heterogeneity Loss to Handle Intersubject and Intrasubject Variability in Cancer

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

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