An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder Article Swipe
Hani Alshahrani
,
Gunjan Sharma
,
Vatsala Anand
,
Sheifali Gupta
,
Adel Sulaiman
,
M. A. Elmagzoub
,
Mana Saleh Al Reshan
,
Asadullah Shaikh
,
Ahmad Taher Azar
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/life13102091
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/life13102091
Bone marrow (BM) is an essential part of the hematopoietic system, which generates all of the body’s blood cells and maintains the body’s overall health and immune system. The classification of bone marrow cells is pivotal in both clinical and research settings because many hematological diseases, such as leukemia, myelodysplastic syndromes, and anemias, are diagnosed based on specific abnormalities in the number, type, or morphology of bone marrow cells. There is a requirement for developing a robust deep-learning algorithm to diagnose bone marrow cells to keep a close check on them. This study proposes a framework for categorizing bone marrow cells into seven classes. In the proposed framework, five transfer learning models—DenseNet121, EfficientNetB5, ResNet50, Xception, and MobileNetV2—are implemented into the bone marrow dataset to classify them into seven classes. The best-performing DenseNet121 model was fine-tuned by adding one batch-normalization layer, one dropout layer, and two dense layers. The proposed fine-tuned DenseNet121 model was optimized using several optimizers, such as AdaGrad, AdaDelta, Adamax, RMSprop, and SGD, along with different batch sizes of 16, 32, 64, and 128. The fine-tuned DenseNet121 model was integrated with an attention mechanism to improve its performance by allowing the model to focus on the most relevant features or regions of the image, which can be particularly beneficial in medical imaging, where certain regions might have critical diagnostic information. The proposed fine-tuned and integrated DenseNet121 achieved the highest accuracy, with a training success rate of 99.97% and a testing success rate of 97.01%. The key hyperparameters, such as batch size, number of epochs, and different optimizers, were all considered for optimizing these pre-trained models to select the best model. This study will help in medical research to effectively classify the BM cells to prevent diseases like leukemia.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/life13102091
- https://www.mdpi.com/2075-1729/13/10/2091/pdf?version=1697810382
- OA Status
- gold
- Cited By
- 18
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387806866
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4387806866Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/life13102091Digital Object Identifier
- Title
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An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological DisorderWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-20Full publication date if available
- Authors
-
Hani Alshahrani, Gunjan Sharma, Vatsala Anand, Sheifali Gupta, Adel Sulaiman, M. A. Elmagzoub, Mana Saleh Al Reshan, Asadullah Shaikh, Ahmad Taher AzarList of authors in order
- Landing page
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https://doi.org/10.3390/life13102091Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-1729/13/10/2091/pdf?version=1697810382Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2075-1729/13/10/2091/pdf?version=1697810382Direct OA link when available
- Concepts
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Bone marrow, Pathology, Transfer of learning, Hematological disorders, Computer science, Medicine, Artificial intelligence, ImmunologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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18Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 10, 2023: 3Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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