ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models
Skin cancer has become a serious disease which has the potential to scale up if it is not identified earlier. It is imperative to detect and give treatment to skin cancer promptly. Diagnosing skin cancer manually takes a lot of time and it is costly, and the probability of false diagnosis has increa...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-10-01
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| Series: | Automatika |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2024.2396167 |
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| author | R. Gomathi S. Gnanavel K.E. Narayana B. Dhiyanesh |
| author_facet | R. Gomathi S. Gnanavel K.E. Narayana B. Dhiyanesh |
| author_sort | R. Gomathi |
| collection | DOAJ |
| description | Skin cancer has become a serious disease which has the potential to scale up if it is not identified earlier. It is imperative to detect and give treatment to skin cancer promptly. Diagnosing skin cancer manually takes a lot of time and it is costly, and the probability of false diagnosis has increased due to the outstanding resemblances among various skin lesions. Enhancing the classification of multi-class lesions of skin needs the development of investigative systems which should be automated. Data augmentation with GANs and Adaptive Conditional Generative Adversarial Network strategies improves performance. The performance is tested using balanced and unbalanced datasets. Using a proper process of augmentation of data, the suggested system attains a 94% accuracy for the VGG16, 93% for the ResNet50 and 94.25% for ResNet101. The process of collaboration of all such methods improves accuracy further to 95%. In summary, the novelty of the work lies in its holistic approach to automated skin lesion classification, incorporating advanced deep learning models, novel data augmentation techniques and comprehensive performance evaluation on real-world datasets. These contributions collectively advance the field of computer-aided diagnosis for the detection of skin cancer and treatment. |
| format | Article |
| id | doaj-art-ae4d3207d4ed4447aa8d7510b0757878 |
| institution | OA Journals |
| issn | 0005-1144 1848-3380 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj-art-ae4d3207d4ed4447aa8d7510b07578782025-08-20T02:07:01ZengTaylor & Francis GroupAutomatika0005-11441848-33802024-10-016541458146810.1080/00051144.2024.2396167ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning modelsR. Gomathi0S. Gnanavel1K.E. Narayana2B. Dhiyanesh3Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, IndiaDepartment of Computing Technologies, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, IndiaDepartment of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai, IndiaDepartment of Computer Science and Engineering - ETech, SRM Institute of Science and Technology, Chennai, IndiaSkin cancer has become a serious disease which has the potential to scale up if it is not identified earlier. It is imperative to detect and give treatment to skin cancer promptly. Diagnosing skin cancer manually takes a lot of time and it is costly, and the probability of false diagnosis has increased due to the outstanding resemblances among various skin lesions. Enhancing the classification of multi-class lesions of skin needs the development of investigative systems which should be automated. Data augmentation with GANs and Adaptive Conditional Generative Adversarial Network strategies improves performance. The performance is tested using balanced and unbalanced datasets. Using a proper process of augmentation of data, the suggested system attains a 94% accuracy for the VGG16, 93% for the ResNet50 and 94.25% for ResNet101. The process of collaboration of all such methods improves accuracy further to 95%. In summary, the novelty of the work lies in its holistic approach to automated skin lesion classification, incorporating advanced deep learning models, novel data augmentation techniques and comprehensive performance evaluation on real-world datasets. These contributions collectively advance the field of computer-aided diagnosis for the detection of skin cancer and treatment.https://www.tandfonline.com/doi/10.1080/00051144.2024.2396167Skin cancerdeep learninghealthcareneural networksgenerative adversarial networksdata augmentation |
| spellingShingle | R. Gomathi S. Gnanavel K.E. Narayana B. Dhiyanesh ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models Automatika Skin cancer deep learning healthcare neural networks generative adversarial networks data augmentation |
| title | ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| title_full | ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| title_fullStr | ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| title_full_unstemmed | ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| title_short | ACGAN: adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| title_sort | acgan adaptive conditional generative adversarial network architecture predicting skin lesion using collaboration of transfer learning models |
| topic | Skin cancer deep learning healthcare neural networks generative adversarial networks data augmentation |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2024.2396167 |
| work_keys_str_mv | AT rgomathi acganadaptiveconditionalgenerativeadversarialnetworkarchitecturepredictingskinlesionusingcollaborationoftransferlearningmodels AT sgnanavel acganadaptiveconditionalgenerativeadversarialnetworkarchitecturepredictingskinlesionusingcollaborationoftransferlearningmodels AT kenarayana acganadaptiveconditionalgenerativeadversarialnetworkarchitecturepredictingskinlesionusingcollaborationoftransferlearningmodels AT bdhiyanesh acganadaptiveconditionalgenerativeadversarialnetworkarchitecturepredictingskinlesionusingcollaborationoftransferlearningmodels |