Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification
Skin cancer, with its rising global prevalence, remains a crucial healthcare challenge, necessitating efficient and early detection for better patient outcomes. While deep convolutional neural networks have advanced image classification, current models struggle with diverse lesion types, variable im...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10772229/ |
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| author | Iftekhar Ahmed Biggo Bushon Routh Md. Saidur Rahman Kohinoor Shadman Sakib Md Mahfuzur Rahman Farag Azzedin |
| author_facet | Iftekhar Ahmed Biggo Bushon Routh Md. Saidur Rahman Kohinoor Shadman Sakib Md Mahfuzur Rahman Farag Azzedin |
| author_sort | Iftekhar Ahmed |
| collection | DOAJ |
| description | Skin cancer, with its rising global prevalence, remains a crucial healthcare challenge, necessitating efficient and early detection for better patient outcomes. While deep convolutional neural networks have advanced image classification, current models struggle with diverse lesion types, variable image quality, and dataset imbalances. Artifacts like hair can further obscure important features. This research addresses the problem and introduces a novel deep learning approach for accurate skin cancer classification by combining ResNet50V2, MobileNetV2, and EfficientNetV2 models. Our proposed architecture leverages the unique feature extraction capabilities of these models. It incorporates an attention mechanism to dynamically emphasize relevant features, enhancing focus, and promoting synergistic interactions among diverse feature sets. As such, our ensemble architectural approach outperforms other state-of-the-art models with high precision, recall, and F1-score metrics. Notably, the model demonstrates significant precision for Dermatofibroma (92% with 96% recall) and Vascular lesions (99% with 99% recall), highlighting its robustness across varied lesion types. Additionally, comprehensive image preprocessing techniques, including image resampling, black-hat filtering, thresholding, morphological closing, inpainting, and overall hair artifact removal, ensure the dataset’s quality and reliability. Validated through statistical significance testing and prototyped with an mHealth solution, this research heralds a significant stride in skin cancer diagnosis with the potential of attention-enhanced ensemble architectures. |
| format | Article |
| id | doaj-art-bcaeea5ab7cb41a2835ee88327fba4e9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bcaeea5ab7cb41a2835ee88327fba4e92025-08-20T02:21:51ZengIEEEIEEE Access2169-35362024-01-011218100918102410.1109/ACCESS.2024.351022410772229Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer ClassificationIftekhar Ahmed0https://orcid.org/0009-0004-6081-0707Biggo Bushon Routh1Md. Saidur Rahman Kohinoor2https://orcid.org/0000-0002-0405-8539Shadman Sakib3https://orcid.org/0000-0002-8794-1414Md Mahfuzur Rahman4https://orcid.org/0000-0002-4334-6402Farag Azzedin5https://orcid.org/0000-0001-9712-439XDepartment of Computer Science and Engineering, Leading University, Sylhet, BangladeshDepartment of Computer Science and Engineering, Leading University, Sylhet, BangladeshDepartment of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaInterdisciplinary Computer Science (InteX) Research Lab, Sylhet, BangladeshDepartment of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaDepartment of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi ArabiaSkin cancer, with its rising global prevalence, remains a crucial healthcare challenge, necessitating efficient and early detection for better patient outcomes. While deep convolutional neural networks have advanced image classification, current models struggle with diverse lesion types, variable image quality, and dataset imbalances. Artifacts like hair can further obscure important features. This research addresses the problem and introduces a novel deep learning approach for accurate skin cancer classification by combining ResNet50V2, MobileNetV2, and EfficientNetV2 models. Our proposed architecture leverages the unique feature extraction capabilities of these models. It incorporates an attention mechanism to dynamically emphasize relevant features, enhancing focus, and promoting synergistic interactions among diverse feature sets. As such, our ensemble architectural approach outperforms other state-of-the-art models with high precision, recall, and F1-score metrics. Notably, the model demonstrates significant precision for Dermatofibroma (92% with 96% recall) and Vascular lesions (99% with 99% recall), highlighting its robustness across varied lesion types. Additionally, comprehensive image preprocessing techniques, including image resampling, black-hat filtering, thresholding, morphological closing, inpainting, and overall hair artifact removal, ensure the dataset’s quality and reliability. Validated through statistical significance testing and prototyped with an mHealth solution, this research heralds a significant stride in skin cancer diagnosis with the potential of attention-enhanced ensemble architectures.https://ieeexplore.ieee.org/document/10772229/Skin cancerdeep learningtransfer learningmulti-model fusionfeature extractionimage preprocessing |
| spellingShingle | Iftekhar Ahmed Biggo Bushon Routh Md. Saidur Rahman Kohinoor Shadman Sakib Md Mahfuzur Rahman Farag Azzedin Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification IEEE Access Skin cancer deep learning transfer learning multi-model fusion feature extraction image preprocessing |
| title | Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification |
| title_full | Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification |
| title_fullStr | Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification |
| title_full_unstemmed | Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification |
| title_short | Multi-Model Attentional Fusion Ensemble for Accurate Skin Cancer Classification |
| title_sort | multi model attentional fusion ensemble for accurate skin cancer classification |
| topic | Skin cancer deep learning transfer learning multi-model fusion feature extraction image preprocessing |
| url | https://ieeexplore.ieee.org/document/10772229/ |
| work_keys_str_mv | AT iftekharahmed multimodelattentionalfusionensembleforaccurateskincancerclassification AT biggobushonrouth multimodelattentionalfusionensembleforaccurateskincancerclassification AT mdsaidurrahmankohinoor multimodelattentionalfusionensembleforaccurateskincancerclassification AT shadmansakib multimodelattentionalfusionensembleforaccurateskincancerclassification AT mdmahfuzurrahman multimodelattentionalfusionensembleforaccurateskincancerclassification AT faragazzedin multimodelattentionalfusionensembleforaccurateskincancerclassification |