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

Anilkumar, K.; Manoj, Vasupalli; Sagi, Tomer

doi:10.1016/j.irbm.2021.05.005

Cited by 39 publications

(15 citation statements)

References 30 publications

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“…Anilkumar et al [ 15 ] purposes for classifying ALL model by DL-based approaches. This analysis utilized DCNN for classifying ALL based on the WHO classifier model without utilizing some image segmentation and feature extraction, which contains intense computation.…”

Section: Related Workmentioning

confidence: 99%

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Hamza

Albraikan

Al-Turaiki

et al. 2022

Computational Intelligence and Neuroscience

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Human-centric biomedical diagnosis (HCBD) becomes a hot research topic in the healthcare sector, which assists physicians in the disease diagnosis and decision-making process. Leukemia is a pathology that affects younger people and adults, instigating early death and a number of other symptoms. Computer-aided detection models are found to be useful for reducing the probability of recommending unsuitable treatments and helping physicians in the disease detection process. Besides, the rapid development of deep learning (DL) models assists in the detection and classification of medical-imaging-related problems. Since the training of DL models necessitates massive datasets, transfer learning models can be employed for image feature extraction. In this view, this study develops an optimal deep transfer learning-based human-centric biomedical diagnosis model for acute lymphoblastic detection (ODLHBD-ALLD). The presented ODLHBD-ALLD model mainly intends to detect and classify acute lymphoblastic leukemia using blood smear images. To accomplish this, the ODLHBD-ALLD model involves the Gabor filtering (GF) technique as a noise removal step. In addition, it makes use of a modified fuzzy c-means (MFCM) based segmentation approach for segmenting the images. Besides, the competitive swarm optimization (CSO) algorithm with the EfficientNetB0 model is utilized as a feature extractor. Lastly, the attention-based long-short term memory (ABiLSTM) model is employed for the proper identification of class labels. For investigating the enhanced performance of the ODLHBD-ALLD approach, a wide range of simulations were executed on open access dataset. The comparative analysis reported the betterment of the ODLHBD-ALLD model over the other existing approaches.

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Section: Related Workmentioning

confidence: 99%

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Hamza

Albraikan

Al-Turaiki

et al. 2022

Computational Intelligence and Neuroscience

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“…However, these methods lack interpretability studies and use limited imaging. By creating large-scale cell annotation datasets, studies have achieved expert-level nucleated cell differential counting (NDC) of bone marrow micrographs or single-cell images using CNNs (27)(28)(29)(30). However, these methods still require manual involvement to obtain the ROIs and locate cellular trails.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning

Wang

Wei²,

Li³

et al. 2022

Front. Oncol.

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Hematopoietic disorders are serious diseases that threaten human health, and the diagnosis of these diseases is essential for treatment. However, traditional diagnosis methods rely on manual operation, which is time consuming and laborious, and examining entire slide is challenging. In this study, we developed a weakly supervised deep learning method for diagnosing malignant hematological diseases requiring only slide-level labels. The method improves efficiency by converting whole-slide image (WSI) patches into low-dimensional feature representations. Then the patch-level features of each WSI are aggregated into slide-level representations by an attention-based network. The model provides final diagnostic predictions based on these slide-level representations. By applying the proposed model to our collection of bone marrow WSIs at different magnifications, we found that an area under the receiver operating characteristic curve of 0.966 on an independent test set can be obtained at 10× magnification. Moreover, the performance on microscopy images can achieve an average accuracy of 94.2% on two publicly available datasets. In conclusion, we have developed a novel method that can achieve fast and accurate diagnosis in different scenarios of hematological disorders.

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“…LHS Vogado proposed a method for leukemia diagnosis that employs CNN for feature extraction followed by SVM‐based classification 13 . K. K. Anilkumar et al categorize B cell and T cell ALL using the pre‐trained networks AlexNet and LeukNet, with a classification accuracy of 94.12% 14 . R. Khandekar et al used an object detection method called You Only Look Once to detect leukemic cells with more than 96% precision 15 .…”

Section: Introductionmentioning

confidence: 99%

Identification of white blood cells for the diagnosis of acute myeloid leukemia

Roy

Ameer

2022

Int J Imaging Syst Tech

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The diagnosis of hematologic malignancies such as leukemia includes identifying malignant white blood cells. Manual microscopic analysis of white blood cells is time-consuming and involves the assistance of medical specialists, and its accuracy may be affected by their abilities. Our objective is to develop an automated system that can identify 15 categories of white blood cells to assist medical practitioners in diagnosing acute myeloid leukemia. The proposed approach uses an ensemble model built with transfer learning-based pretrained networks to categorize 15 kinds of white blood cells. An over-sampling strategy is used to alleviate the problem of class imbalance, followed by data augmentation. For experimentation, a microscopic blood image dataset containing 18 365 cells images from 200 individuals representing 15 different forms of leukocytes is used. For cells with more than 400 images available, the suggested technique achieves an F1 score of more than 91%. Myeloblasts, which are frequent in myeloid leukemias and are detected in the peripheral blood, are recognized with an average precision of 95.74%. This work describes an image processing strategy that employs ensemble learning to assist in diagnosing acute myeloid leukemia by classifying 15 different types of leukocytes.Experiments show that our technique is both practical and effective. Extensive research supports the use of the leukocyte classifier in real-world medical applications, such as assisting clinicians in diagnosing diseases and decreasing human resource requirements.

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

Cited by 39 publications

References 30 publications

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Optimal Deep Transfer Learning-Based Human-Centric Biomedical Diagnosis for Acute Lymphoblastic Leukemia Detection

Efficient and Highly Accurate Diagnosis of Malignant Hematological Diseases Based on Whole-Slide Images Using Deep Learning

Identification of white blood cells for the diagnosis of acute myeloid leukemia

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