Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm
Abstract Skin cancer, particularly melanoma, poses a critical global health challenge due to its high mortality rate. Early and precise detection is vital for effective treatment and better prognosis. Recent advancements in deep learning have shown significant promise in medical image analysis, incl...
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| Format: | Article |
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SpringerOpen
2025-07-01
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| Series: | Journal of Electrical Systems and Information Technology |
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| Online Access: | https://doi.org/10.1186/s43067-025-00243-8 |
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| author | Amany M. Sarhan Hesham A. Ali Shady Yasser Mohamed Gobara Ahmed A. Kandil Ghada Sherif Esraa Moustafa |
| author_facet | Amany M. Sarhan Hesham A. Ali Shady Yasser Mohamed Gobara Ahmed A. Kandil Ghada Sherif Esraa Moustafa |
| author_sort | Amany M. Sarhan |
| collection | DOAJ |
| description | Abstract Skin cancer, particularly melanoma, poses a critical global health challenge due to its high mortality rate. Early and precise detection is vital for effective treatment and better prognosis. Recent advancements in deep learning have shown significant promise in medical image analysis, including skin cancer classification. This study investigates the automated classification of skin lesions using the HAM10000 dataset, which features high-resolution images across seven distinct classes. We focus on utilizing deep learning, specifically convolutional neural networks (CNNs), to enhance the accuracy of skin lesion classification. Our research examines several CNN architectures, including XceptionNet, DenseNet201, DenseNet169, DenseNet121, MobileNetV2, and GoogleNet, alongside a customized CNN model tailored for skin cancer classification. We incorporate techniques such as data augmentation and transfer learning to further refine model performance. Hyperparameter optimization is achieved using the Ant Colony Optimization algorithm. The proposed models are evaluated on the HAM10000 dataset with standard metrics; accuracy, precision, recall, and F1-score. Our results highlight the effectiveness of deep learning in distinguishing between various skin cancer types attaining values of 96.5%, 97.0%, and 97.0% for accuracy, precision, recall, and F1-score, respectively, showing improvements over existing state-of-the-art methods in both classification accuracy. These findings offer significant implications for dermatology and healthcare by facilitating automated skin cancer classification, potentially aiding dermatologists in early diagnosis and improving patient outcomes. Additionally, this framework provides a foundation for future research in applying deep learning to medical image analysis and healthcare diagnostics. |
| format | Article |
| id | doaj-art-58dbfd656a4b4cbeb5ab0c8fcfd035b5 |
| institution | Kabale University |
| issn | 2314-7172 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Electrical Systems and Information Technology |
| spelling | doaj-art-58dbfd656a4b4cbeb5ab0c8fcfd035b52025-08-20T04:01:52ZengSpringerOpenJournal of Electrical Systems and Information Technology2314-71722025-07-0112112510.1186/s43067-025-00243-8Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithmAmany M. Sarhan0Hesham A. Ali1Shady Yasser2Mohamed Gobara3Ahmed A. Kandil4Ghada Sherif5Esraa Moustafa6Department of Computer and Control Engineering, Tanta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityDepartment of Artificial Intelligence, Faculty of Artificial Intelligence, Delta UniversityAbstract Skin cancer, particularly melanoma, poses a critical global health challenge due to its high mortality rate. Early and precise detection is vital for effective treatment and better prognosis. Recent advancements in deep learning have shown significant promise in medical image analysis, including skin cancer classification. This study investigates the automated classification of skin lesions using the HAM10000 dataset, which features high-resolution images across seven distinct classes. We focus on utilizing deep learning, specifically convolutional neural networks (CNNs), to enhance the accuracy of skin lesion classification. Our research examines several CNN architectures, including XceptionNet, DenseNet201, DenseNet169, DenseNet121, MobileNetV2, and GoogleNet, alongside a customized CNN model tailored for skin cancer classification. We incorporate techniques such as data augmentation and transfer learning to further refine model performance. Hyperparameter optimization is achieved using the Ant Colony Optimization algorithm. The proposed models are evaluated on the HAM10000 dataset with standard metrics; accuracy, precision, recall, and F1-score. Our results highlight the effectiveness of deep learning in distinguishing between various skin cancer types attaining values of 96.5%, 97.0%, and 97.0% for accuracy, precision, recall, and F1-score, respectively, showing improvements over existing state-of-the-art methods in both classification accuracy. These findings offer significant implications for dermatology and healthcare by facilitating automated skin cancer classification, potentially aiding dermatologists in early diagnosis and improving patient outcomes. Additionally, this framework provides a foundation for future research in applying deep learning to medical image analysis and healthcare diagnostics.https://doi.org/10.1186/s43067-025-00243-8Skin cancerDeep learningConvolutional neural networksCNNsHAM10000Medical image analysis |
| spellingShingle | Amany M. Sarhan Hesham A. Ali Shady Yasser Mohamed Gobara Ahmed A. Kandil Ghada Sherif Esraa Moustafa Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm Journal of Electrical Systems and Information Technology Skin cancer Deep learning Convolutional neural networks CNNs HAM10000 Medical image analysis |
| title | Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| title_full | Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| title_fullStr | Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| title_full_unstemmed | Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| title_short | Achieving high-accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| title_sort | achieving high accuracy skin cancer classification with deep learning optimized by ant colony algorithm |
| topic | Skin cancer Deep learning Convolutional neural networks CNNs HAM10000 Medical image analysis |
| url | https://doi.org/10.1186/s43067-025-00243-8 |
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