A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques

Raina, Rohini; Gondhi, Naveen Kumar; Chaahat,; Singh, Dilbag; Kaur, Manjit; Lee, Heung-No

doi:10.1007/s11831-022-09796-7

Cited by 29 publications

(12 citation statements)

References 80 publications

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“…The previously published surveys ( Das et al, 2022 ; Khan et al, 2021 ; SivaRao & Rao, 2023 ; Raina et al, 2023 ; Umamaheswari & Geetha, 2019 ; Asghar et al, 2023 ) do not pay more attention to discussing the usability of the publicly available datasets. This issue is consistently highlighted as a significant challenge in developing and validating new models.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…This issue is consistently highlighted as a significant challenge in developing and validating new models. Another common challenge mentioned in the surveys ( Das et al, 2022 ; Khan et al, 2021 ; Anilkumar, Manoj & Sagi, 2023 ; Raina et al, 2023 ; Rao & Rao, 2023 ; Umamaheswari & Geetha, 2019 ; More & Sugandhi, 2023 ; Al-Dulaimi & Makki, 2023 ; Veeraiah, Alotaibi & Subahi, 2023 ) is the inherent complexity in microscopic images. This shows a demanding need for robust algorithms that can handle such complexities.…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…This shows a demanding need for robust algorithms that can handle such complexities. Additionally, the inconsistency in diagnosis depending on the pathologist's experience is emphasized in surveys ( Das et al, 2022 ; Raina et al, 2023 ; Rao & Rao, 2023 ; Umamaheswari & Geetha, 2019 ; More & Sugandhi, 2023 ; Veeraiah, Alotaibi & Subahi, 2023 ), highlighting the importance of automated systems for more consistent and accurate diagnosis. Each survey also brings unique contributions like ( SivaRao & Rao, 2023 ) and ( Bhargavi et al, 2023 ), which uniquely focus on using SegNet, EfficientNet, and XGBoost for WBC classification, providing a different perspective from other surveys.…”

Section: Analysis and Discussionmentioning

confidence: 99%

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Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

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Background Blood diseases such as leukemia, anemia, lymphoma, and thalassemia are hematological disorders that relate to abnormalities in the morphology and concentration of blood elements, specifically white blood cells (WBC) and red blood cells (RBC). Accurate and efficient diagnosis of these conditions significantly depends on the expertise of hematologists and pathologists. To assist the pathologist in the diagnostic process, there has been growing interest in utilizing computer-aided diagnostic (CAD) techniques, particularly those using medical image processing and machine learning algorithms. Previous surveys in this domain have been narrowly focused, often only addressing specific areas like segmentation or classification but lacking a holistic view like segmentation, classification, feature extraction, dataset utilization, evaluation matrices, etc. Methodology This survey aims to provide a comprehensive and systematic review of existing literature and research work in the field of blood image analysis using deep learning techniques. It particularly focuses on medical image processing techniques and deep learning algorithms that excel in the morphological characterization of WBCs and RBCs. The review is structured to cover four main areas: segmentation techniques, classification methodologies, descriptive feature selection, evaluation parameters, and dataset selection for the analysis of WBCs and RBCs. Results Our analysis reveals several interesting trends and preferences among researchers. Regarding dataset selection, approximately 50% of research related to WBC segmentation and 60% for RBC segmentation opted for manually obtaining images rather than using a predefined dataset. When it comes to classification, 45% of the previous work on WBCs chose the ALL-IDB dataset, while a significant 73% of researchers focused on RBC classification decided to manually obtain images from medical institutions instead of utilizing predefined datasets. In terms of feature selection for classification, morphological features were the most popular, being chosen in 55% and 80% of studies related to WBC and RBC classification, respectively. Conclusion The diagnostic accuracy for blood-related diseases like leukemia, anemia, lymphoma, and thalassemia can be significantly enhanced through the effective use of CAD techniques, which have evolved considerably in recent years. This survey provides a broad and in-depth review of the techniques being employed, from image segmentation to classification, feature selection, utilization of evaluation matrices, and dataset selection. The inconsistency in dataset selection suggests a need for standardized, high-quality datasets to strengthen the diagnostic capabilities of these techniques further. Additionally, the popularity of morphological features indicates that future research could further explore and innovate in this direction.

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Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

Section: Analysis and Discussionmentioning

confidence: 99%

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Blood cell image segmentation and classification: a systematic review

Shahzad,

Ali,

Shirazi

et al. 2024

PeerJ Computer Science

View full text Add to dashboard Cite

show abstract

“…Deep learning does not require manual feature extraction, but it performs data filtering and feature extraction automatically, which can greatly save time [12][13][14][15]. Specially, deep learning has excelled and captured the attention of all over the world in recent years in the field of analysis of medical images, particularly in utilizing convolutional neural network (CNN) to construct end-to-end networks for image analysis [16]. CNN, which is one of the most significant networks in deep learning, can be able to construct multi-layered convolutional network for the purpose of learning and extracting features [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A lightweight white blood cells detection network based on CenterNet and feature fusion modules

Wu,

Zou,

Zuo

et al. 2024

Meas. Sci. Technol.

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White blood cells detection is significant to the diagnosis of many diseases. However, the detection accuracy can be influenced by the significant differences in color, size and morphology of white blood cells in the images. In this paper, an improved CenterNet with smaller model size, fewer parameters, and lower computational complexity is proposed for the white blood cells accurate detection. Firstly, the lightweight backbone GhostNetv2 is selected to reduce the model size, parameters and computational complexity of the network. Then, a feature pyramid network with the ECA attention mechanism and the multi-scale feature extraction modules is constructed to enhance the capability of feature extraction and focus more on white blood cells. Finally, the heatmap loss function is modified by proposing an improved MSE loss function to enhance the fitting ability between the predicted values and the ground truth of the heatmap. Experimental results show that the model size of the proposed lightweight CenterNet is only 19.9MB, and the mAP.5 is 97.36%. The model size is reduced by 84% while the mAP.5 and FPS are increased by 0.7% and 10.4 compared to the original CenterNet. Moreover, the detection accuracy of the proposed lightweight CenterNet is comparable to the existing mainstream networks and its detection performance on different datasets is good, while the model size, parameters and computational complexity of the network is significantly reduced, and can be used for white blood cells detection effectively.

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“…Cancer ranks second in mortality behind cardiovascular disease. Cancer such as breast, 1 blood 2 and brain 3 have the worst likelihood of being cured among all types of cancer. The number of newly diagnosed instances of brain tumours in 2019 reached 86 000, and about 17 000 individuals lost their lives to this illness in 2019 4 .…”

Section: Introductionmentioning

confidence: 99%

MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images

Agrawal,

Choudhary,

Shankar

et al. 2023

Int J Imaging Syst Tech

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One of the most fatal and prevalent diseases of the central nervous system is a brain tumour. Different subgrades exist for each type of brain tumour because of the broad variety of brain tumours and tumour pathologies. Manual diagnosis may be error‐prone and time‐consuming, both of which are becoming more challenging as the medical community's workload grows. There is a need for automatic diagnosis. In this study, we have proposed a deep learning model (MultiFeNet) based on a convolutional neural network for the classification of brain tumours. MultiFeNet uses multi‐scale feature scaling for feature extraction in magnetic resonance imaging (MRI) images. Multi‐scaling helps to learn the better feature representation of the MRI image for enhanced model performance. To evaluate the proposed model, 3064 MRI scans of three distinct categories of brain tumours (meningiomas, gliomas and pituitary tumours) were used. The MultiFeNet obtained 96.4% sensitivity, 96.4% F1‐score, 96.4% precision and 96.4% accuracy on the benchmark Figshare dataset. In addition, an ablation study is conducted with the objective of evaluating the role of multi‐scaling in model performance.

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A Systematic Review on Acute Leukemia Detection Using Deep Learning Techniques

Cited by 29 publications

References 80 publications

Blood cell image segmentation and classification: a systematic review

Blood cell image segmentation and classification: a systematic review

A lightweight white blood cells detection network based on CenterNet and feature fusion modules

MultiFeNet: Multi‐scale feature scaling in deep neural network for the brain tumour classification in MRI images

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