Ahmed, Khaja Raoufuddin and A. Jalil, Siti Zura and Usman, Sahnius (2023) Improved squirrel optimization based generative adversarial network for skin cancer classification. In: 2023 IEEE 2nd National Biomedical Engineering Conference (NBEC), 5 September 2023-7 September 2023, Melaka, Malaysia.
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Official URL: http://dx.doi.org/10.1109/NBEC58134.2023.10352631
Abstract
Cancer is one of the most severe threats to global health in today's world and it cannot be fully cured. Thus, the early detection of the disease helps to reduce the risk and enhances the lifetime of the patient. There are several automatic methods for the detection of skin cancer, wherein accurate classification with minimal computation burden is more challenging task. Hence, this research introduces a novel optimized deep learning technique for the classification of skin cancer using Improved Squirrel based Generative Adversarial Network (ImSq-GAN). The performance of the proposed method is evaluated using Accuracy, F-Score, MSE, Precision, Recall, and Specificity and acquired the values of 0.96, 0.97, 0.04, 0.99, 0.97, and 0.99 respectively.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | chaotic chebyshev, deep learning, generative adversarial network, improved squirrel optimization, Otsu threshold, skin cancer, skin lesion segmentation |
| Subjects: | T Technology > T Technology (General) |
| Divisions: | Razak School of Engineering and Advanced Technology |
| ID Code: | 107763 |
| Deposited By: | Widya Wahid |
| Deposited On: | 02 Oct 2024 07:22 |
| Last Modified: | 02 Oct 2024 07:22 |
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