The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach
Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present s...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10318035/ |
| _version_ | 1797473442613690368 |
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| author | Mudassir Saeed Asma Naseer Hassan Masood Shafiq Ur Rehman Volker Gruhn |
| author_facet | Mudassir Saeed Asma Naseer Hassan Masood Shafiq Ur Rehman Volker Gruhn |
| author_sort | Mudassir Saeed |
| collection | DOAJ |
| description | Skin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy. |
| first_indexed | 2024-03-09T20:15:29Z |
| format | Article |
| id | doaj.art-6446982652f245b0bfad0cb16453e8c5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-09T20:15:29Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6446982652f245b0bfad0cb16453e8c52023-11-24T00:02:04ZengIEEEIEEE Access2169-35362023-01-011113033013034410.1109/ACCESS.2023.333262810318035The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning ApproachMudassir Saeed0https://orcid.org/0009-0000-1429-7085Asma Naseer1https://orcid.org/0000-0002-5980-0526Hassan Masood2Shafiq Ur Rehman3https://orcid.org/0009-0000-2677-0108Volker Gruhn4Department of Computer Science, National University of Computer and Emerging Sciences, Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Lahore, PakistanDepartment of Computer Science, National University of Computer and Emerging Sciences, Lahore, PakistanCollege of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaDepartment of Software Engineering, University of Duisburg–Essen, Essen, GermanySkin cancer, particularly the malignant melanoma subtype, is widely recognized as a highly lethal form of cancer characterized by abnormal melanocyte cell growth. However, diagnosing and classifying skin lesions, as well as automatically recognizing malignant tumors from dermoscopy images, present significant challenges. To address this challenge, our study employs variants of Convolutional Neural Networks (CNNs) to effectively diagnose and classify various skin lesion types using the latest benchmark datasets ISIC 2019 and 2020. The dataset underwent rigorous preprocessing, which involves employing advanced Generative Artificial Intelligence (AI) techniques i.e., Generative Adversarial Networks (GANs) and Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), for augmentation. These generative techniques are carefully evaluated and compared for their effectiveness. Our CNN-based approach involves aggregating results from multiple transfer learning models, including VGG16, VGG19, SVM along with a hybrid model in combination of VGG19 and SVM. On ISIC 2019, we have achieved promising accuracies of 92% for VGG16 and 93% for VGG19. Notably, the hybrid VGG19+SVM model exhibits the highest accuracy of 96%. On ISIC 2020, VGG16, VGG19, and SVM achieves accuracies of 90%, 92%, and 92%, respectively. Our findings underscore the potential of generative AI for augmentation, and the efficacy of CNN-based transfer learning models in improving skin cancer classification accuracy.https://ieeexplore.ieee.org/document/10318035/Skin cancerCNNsVGG16VGG19GANESRGAN |
| spellingShingle | Mudassir Saeed Asma Naseer Hassan Masood Shafiq Ur Rehman Volker Gruhn The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach IEEE Access Skin cancer CNNs VGG16 VGG19 GAN ESRGAN |
| title | The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach |
| title_full | The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach |
| title_fullStr | The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach |
| title_full_unstemmed | The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach |
| title_short | The Power of Generative AI to Augment for Enhanced Skin Cancer Classification: A Deep Learning Approach |
| title_sort | power of generative ai to augment for enhanced skin cancer classification a deep learning approach |
| topic | Skin cancer CNNs VGG16 VGG19 GAN ESRGAN |
| url | https://ieeexplore.ieee.org/document/10318035/ |
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