Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis

Ghane, Narjes; Vard, Alireza; Talebi, Ardeshir; Nematollahy, Pardis

doi:10.17179/excli2019-1292

Cited by 5 publications

(3 citation statements)

References 33 publications

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“…Presently, CNNs are increasingly deployed in the classification and diagnosis of medical image issues ( 2 , 14 , 41 ). In addition, automatic myeloid cell classification systems include myeloid cell classifiers with rejection options ( 35 ), computer-aided diagnostic methods based on microscopic image processing ( 36 ), AI-based automated analysis systems (Morphogo) ( 39 ), novel NN classifiers for classifying myeloid cells based on their nuclei features ( 34 ), microfluidics-based and multimodal imaging for classification and detection system ( 38 ), etc. The classification efficacy of the aforementioned systems, as gauged by metrics like accuracy, recall, sensitivity, specificity, and F-1 score, surpasses that of existing classifiers.…”

Section: Discussionmentioning

confidence: 99%

“…Ghane et al ( 36 ) proposed a straightforward and efficient computer-aided diagnostic (CAD) method based on microscopic image processing. Their approach introduces a novel combination of both typical and innovative features aimed at classifying chronic myeloid leukemia (CML) cells and categorizing bone marrow cells through an effective decision tree classifier.…”

Section: Automation Of Bone Marrow Cell Morphology Classificationmentioning

confidence: 99%

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Recent advancements in machine learning for bone marrow cell morphology analysis

Lin,

Chen,

Chen

2024

Front. Med.

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As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.

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Section: Discussionmentioning

confidence: 99%

Section: Automation Of Bone Marrow Cell Morphology Classificationmentioning

confidence: 99%

Recent advancements in machine learning for bone marrow cell morphology analysis

Lin,

Chen,

Chen

2024

Front. Med.

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“…In this publication, Ghane et al [ 10 ] provide an easy and successful approach to identifying efficient CML cells. To take photos of their data set, a Nikon1 V1 camera was mounted on an Eclipse 50i Nikon microscope with a zoom of 1000.…”

Section: Literature Reviewmentioning

confidence: 99%

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

Das

Islam

Neha

et al. 2021

Contrast Media & Molecular Imaging

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Inside the bone marrow, plasma cells are created, and they are a type of white blood cells. They are made from B lymphocytes. Antigens are produced by plasma cells to combat bacteria and viruses and prevent inflammation and illness. Multiple myeloma is a plasma cell cancer that starts in the bone marrow and causes the formation of abnormal plasma cells. Multiple myeloma is firmly identified by examining bone marrow samples under a microscope for myeloma cells. To diagnose myeloma cells, pathologists have to be very selective. Furthermore, because the ultimate decision is based on human sight and opinion, there is a possibility of error in the result. The nobility of this research is that it provides a computer-assisted technique for recognizing and detecting myeloma cells in bone marrow smears. For recognizing purposes, we have used Mask-Recurrent Convolutional Neural Network, and for detection purposes, Efficient Net B3 has been used. There are already many studies on white blood cell cancer, but very few with both segmentation and classification. We have designed two models. One is for recognizing myeloma cells, and the other is for differentiating them from nonmyeloma cells. Also, a new data set has been made from the multiple myeloma data sets, which has been used in our classification model. This research focuses on hybrid segmentation models and increases the accuracy level of the classification model. Both of our models are trained pretty well, where the Mask-RCNN model gives a mean average precision (mAP) of 93% and the Efficient Net B3 model gives 94.68% accuracy. The result of this research indicates that the Mask-RCNN model can recognize multiple myeloma and Efficient Net B3 can distinguish between myeloma and nonmyeloma cells and beats most of the state of the art in myeloma recognition and detection.

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AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network

Huang¹,

Guang²,

Li³

et al. 2020

Medicine

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Leukemia diagnosis based on bone marrow cell morphology primarily relies on the manual microscopy of bone marrow smears. However, this method is greatly affected by subjective factors and tends to lead to misdiagnosis. This study proposes using bone marrow cell microscopy images and employs convolutional neural network (CNN) combined with transfer learning to establish an objective, rapid, and accurate method for classification and diagnosis of LKA (AML, ALL, and CML). We collected cell microscopy images of 104 bone marrow smears (including 18 healthy subjects, 53 AML patients, 23 ALL patients, and 18 CML patients). The perfect reflection algorithm and a self-adaptive filter algorithm were first used for preprocessing of bone marrow cell images collected from experiments. Subsequently, 3 CNN frameworks (Inception-V3, ResNet50, and DenseNet121) were used to construct classification models for the raw dataset and preprocessed dataset. Transfer learning was used to improve the prediction accuracy of the model. Results showed that the DenseNet121 model based on the preprocessed dataset provided the best classification results, with a prediction accuracy of 74.8%. The prediction accuracy of the DenseNet121 model that was obtained by transfer learning optimization was 95.3%, which was increased by 20.5%. In this model, the prediction accuracies of the normal groups, AML, ALL, and CML were 90%, 99%, 97%, and 95%, respectively. The results showed that the leukemic cell morphology classification and diagnosis based on CNN combined with transfer learning is feasible. Compared with conventional manual microscopy, this method is more rapid, accurate, and objective.

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Classification of chronic myeloid leukemia cell subtypes based on microscopic image analysis

Cited by 5 publications

References 33 publications

Recent advancements in machine learning for bone marrow cell morphology analysis

Recent advancements in machine learning for bone marrow cell morphology analysis

Towards the Segmentation and Classification of White Blood Cell Cancer Using Hybrid Mask-Recurrent Neural Network and Transfer Learning

AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network

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