Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images
Abstract Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body’s blood cells and maintaining the overall immune and health system. Red and yellow BM are the two various kinds of BM. A comprehensive identification of these cells assists in the primary and preci...
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Nature Portfolio
2025-04-01
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| Online Access: | https://doi.org/10.1038/s41598-025-89529-5 |
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| author | Khaled Tarmissi Jamal Alsamri Mashael Maashi Mashael M. Asiri Abdulsamad Ebrahim Yahya Abdulwhab Alkharashi Monir Abdullah Marwa Obayya |
| author_facet | Khaled Tarmissi Jamal Alsamri Mashael Maashi Mashael M. Asiri Abdulsamad Ebrahim Yahya Abdulwhab Alkharashi Monir Abdullah Marwa Obayya |
| author_sort | Khaled Tarmissi |
| collection | DOAJ |
| description | Abstract Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body’s blood cells and maintaining the overall immune and health system. Red and yellow BM are the two various kinds of BM. A comprehensive identification of these cells assists in the primary and precise recognition of these disorders. The recognition and identification of BM cells are crucial bases for haematology diagnostics. Physical study of BM detection and classification presently performed in medical laboratories can be primarily insufficient owing to various factors, such as prolonged and challenging. Recently, with the fast growth of deep learning (DL) and machine learning (ML) methods, object detection methods have been progressively used for cell detection. DL is a secondary domain of artificial intelligence (AI) methods able to spontaneously assess delicate graphical features to create exact predictions that have been newly popularized in various imaging-related tasks. This study proposes a Multimodal Transfer Learning with Snake Optimization on Bone Marrow Cell Classification (MTLSO-BMCC) technique using biomedical histopathological images. The main intention of the MTLSO-BMCC technique is to identify and classify BM cells utilizing HI. To achieve this, the presented MTLSO-BMCC method initially performs image preprocessing using a median filter (MF) for noise removal. Besides, the multimodal feature extraction process is accomplished in InceptionV3, Deep SqueezeNet, and SE-DenseNet models. The presented MTLSO-BMCC technique employs the hybrid kernel extreme learning machine (HKELM) method for the BM classification method. Finally, the snake optimization algorithm (SOA) is implemented to tune the parameter of the HKELM model. A widespread MTLSO-BMCC methodology simulation is accomplished under the BM Cell Classification dataset. The experimental validation of the MTLSO-BMCC methodology portrayed a superior accuracy value of 98.60% over existing approaches. |
| format | Article |
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| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-1e24b5a4ceaf403babcadb400c6b460a2025-08-20T02:20:01ZengNature PortfolioScientific Reports2045-23222025-04-0115112210.1038/s41598-025-89529-5Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological imagesKhaled Tarmissi0Jamal Alsamri1Mashael Maashi2Mashael M. Asiri3Abdulsamad Ebrahim Yahya4Abdulwhab Alkharashi5Monir Abdullah6Marwa Obayya7Department of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura UniversityDepartment of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman UniversityDepartment of Software Engineering, College of Computer and Information Sciences, King Saud UniversityDepartment of Computer Science, Applied College at Mahayil, King Khalid UniversityDepartment of Information Technology, College of Computing and Information Technology, Northern Border UniversityDepartment of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of BishaDepartment of Computer Science, College of Computing and Informatics, Saudi Electronic UniversityDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm-AlQura UniversityAbstract Bone marrow (BM) plays a crucial role in the hematopoietic process, producing all of the body’s blood cells and maintaining the overall immune and health system. Red and yellow BM are the two various kinds of BM. A comprehensive identification of these cells assists in the primary and precise recognition of these disorders. The recognition and identification of BM cells are crucial bases for haematology diagnostics. Physical study of BM detection and classification presently performed in medical laboratories can be primarily insufficient owing to various factors, such as prolonged and challenging. Recently, with the fast growth of deep learning (DL) and machine learning (ML) methods, object detection methods have been progressively used for cell detection. DL is a secondary domain of artificial intelligence (AI) methods able to spontaneously assess delicate graphical features to create exact predictions that have been newly popularized in various imaging-related tasks. This study proposes a Multimodal Transfer Learning with Snake Optimization on Bone Marrow Cell Classification (MTLSO-BMCC) technique using biomedical histopathological images. The main intention of the MTLSO-BMCC technique is to identify and classify BM cells utilizing HI. To achieve this, the presented MTLSO-BMCC method initially performs image preprocessing using a median filter (MF) for noise removal. Besides, the multimodal feature extraction process is accomplished in InceptionV3, Deep SqueezeNet, and SE-DenseNet models. The presented MTLSO-BMCC technique employs the hybrid kernel extreme learning machine (HKELM) method for the BM classification method. Finally, the snake optimization algorithm (SOA) is implemented to tune the parameter of the HKELM model. A widespread MTLSO-BMCC methodology simulation is accomplished under the BM Cell Classification dataset. The experimental validation of the MTLSO-BMCC methodology portrayed a superior accuracy value of 98.60% over existing approaches.https://doi.org/10.1038/s41598-025-89529-5Snake optimization algorithmBone marrow cell classificationMultimodal feature extractionHistopathological imagesImage preprocessing |
| spellingShingle | Khaled Tarmissi Jamal Alsamri Mashael Maashi Mashael M. Asiri Abdulsamad Ebrahim Yahya Abdulwhab Alkharashi Monir Abdullah Marwa Obayya Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images Scientific Reports Snake optimization algorithm Bone marrow cell classification Multimodal feature extraction Histopathological images Image preprocessing |
| title | Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| title_full | Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| title_fullStr | Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| title_full_unstemmed | Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| title_short | Multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| title_sort | multimodal representations of transfer learning with snake optimization algorithm on bone marrow cell classification using biomedical histopathological images |
| topic | Snake optimization algorithm Bone marrow cell classification Multimodal feature extraction Histopathological images Image preprocessing |
| url | https://doi.org/10.1038/s41598-025-89529-5 |
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