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

Mondal, Chayan; Hasan, Md. Kamrul; Jawad, Md. Tasnim; Dutta, Aishwariya; Islam, Md. Rabiul; Awal, Md. Abdul; Ahmad, Mohiuddin

doi:10.48550/arxiv.2105.03995

Cited by 8 publications

(11 citation statements)

References 39 publications

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“…The recommended weighted ensemble model under [21] produced a balanced F1-score of 88.6%, a symmetrical accuracy of 86.2%, and an AUC of 0.941 in the initial testing dataset utilizing the ensemble candidates' kappa values as their weighting. The gradients represent higher maps in the qualitative results showing that the presented paradigm has a focused learned region.…”

Section: Related Workmentioning

confidence: 99%

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Veeraiah¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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Leukemia, often called blood cancer, is a disease that primarily affects white blood cells (WBCs), which harms a person's tissues and plasma. This condition may be fatal when if it is not diagnosed and recognized at an early stage. The physical technique and lab procedures for Leukaemia identification are considered time-consuming. It is crucial to use a quick and unexpected way to identify different forms of Leukaemia. Timely screening of the morphologies of immature cells is essential for reducing the severity of the disease and reducing the number of people who require treatment. Various deep-learning (DL) model-based segmentation and categorization techniques have already been introduced, although they still have certain drawbacks. In order to enhance feature extraction and classification in such a practical way, Mayfly optimization with Generative Adversarial Network (MayGAN) is introduced in this research. Furthermore, Generative Adversarial System (GAS) is integrated with Principal Component Analysis (PCA) in the feature-extracted model to classify the type of blood cancer in the data. The semantic technique and morphological procedures using geometric features are used to segment the cells that makeup Leukaemia. Acute lymphocytic Leukaemia (ALL), acute myelogenous Leukaemia (AML), chronic lymphocytic Leukaemia (CLL), chronic myelogenous Leukaemia (CML), and aberrant White Blood Cancers (WBCs) are all successfully classified by the proposed MayGAN model. The proposed MayGAN identifies the abnormal activity in the WBC, considering the geometric features. Compared with the state-of-the-art methods, the proposed MayGAN achieves 99.8% accuracy, 98.5% precision, 99.7% recall, 97.4% F1-score, and 98.5% Dice similarity coefficient (DSC).

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Section: Related Workmentioning

confidence: 99%

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Veeraiah¹,

Alotaibi²,

Subahi³

2023

Computer Systems Science and Engineering

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“…Mondal et al 38 introduced weighted ensemble technique for detection of ALL using blood microscopic images. Furthermore, authentic as well as precise detection of ALL was attained on basis of features selected.…”

Section: Literature Surveymentioning

confidence: 99%

“…The comparative analysis of introduced Taylor PMBO + DRN approach is carried out by comparing the devised technique with other existing ALL detection and classification algorithms, namely, ADBRF, 2 roughset theory, 10 SVM, 28 Taylor-MBO based SVM, 41 deep CNN 38…”

Section: Comparative Techniquesmentioning

confidence: 99%

Taylor political monarch butterfly optimization driven deep learning model for acute lymphoblastic leukemia detection and severity classification using blood smear images

Bai

Perumal

2022

Concurrency and Computation

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An image processing and classification become an exciting research region for indicative probe because of the expansion of digital microscopic imaging. Acute lymphoblastic leukemia (ALL) is caused owing to broken in bone marrow, which is general among the people around the biosphere. A leukemia generally affects blood creating organs and hence, easily spread to other tissues by blood. The hematologists afflict more to differentiate leukemia existence in body of patients based on blood smears. Hence, in this article, Taylor political monarch butterfly optimization-enabled deep residual neural network (Taylor PMBO-enabled DRN) is devised for ALL classification. Segmentation is the process of separating the images into small pixels. A deep joint segmentation model effectively enhances the detection accuracy and it has better segmentation capability. For a better classification procedure, the important features, such as statistical, texture, and grid features, are extracted. For ALL detection and classification processes, a deep residual neural network (DRN) classifier is trained by the Taylor PMBO.

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“…Mondal et al introduced a method for classifying ALL using a weighted ensemble of CNN [19]. First, pre-processing, including augmentation, rebalancing and RoI extraction, was performed on the images.…”

Section: Previous Related Workmentioning

confidence: 99%

Classification of Acute Lymphocytic Leukemia (ALL) using Ventral-Dorsal and bottleneck Attention-based Network(VDBAN)

Zolfaghari

Sajedi

2022

Preprint

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One of the diseases with a high mortality rate worldwide is leukemia or blood cancer. Therefore, a timely and reliable diagnosis of leukemia is essential. Today, Artificial Intelligence and Deep Learning methods can automatically, quickly, and cost-effectively identify many diseases. In this paper, the state-of-the-art Convolutional Neural Network (CNN)based on the proposed attention blocks is introduced for the automatic classification of Acute Lymphoblastic Leukemia (ALL) and normal (healthy) cell images. Before sending images to the network, the preprocessing operation, including data augmentation and White Blood Cells (WBCs, or Leuko cytes) segmentation by Circular Hough Transform (CHT), is performed. Then, the proposed network attention blocks are designed and implemented based on the ventral and dorsal pathways of the occipital lobe that process visual information in the brain. The proposed network is ResNet18, in which the VentralDorsal Attention Block (VDAB) is embedded inside its blocks and the Bottleneck Attention Block (BAB) between them. Therefore, it is called Ventral-Dorsal and Bottleneck Attention Network (VDBAN). We designed three networks, including the first model (ResNet18), the second model (ResNet18 + VDAB) and the proposed model ((ResNet18+ VDAB + BAB) or VDBAN), and trained and tested them with similar parameters on the ALL-IDB2 database. The simulation results showed that the proposed model had obtained the highest value in all performance metrics compared to the other two models in the test step.

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

Cited by 8 publications

References 39 publications

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

MayGAN: Mayfly Optimization with Generative Adversarial Network-Based Deep Learning Method to Classify Leukemia Form Blood Smear Images

Taylor political monarch butterfly optimization driven deep learning model for acute lymphoblastic leukemia detection and severity classification using blood smear images

Classification of Acute Lymphocytic Leukemia (ALL) using Ventral-Dorsal and bottleneck Attention-based Network(VDBAN)

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