ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging
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arXiv
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866929284469227520 |
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| author | Dhibar, Sibasish |
| author_facet | Dhibar, Sibasish |
| contents | Skin cancer is a crucial health issue that requires timely detection for higher survival rates. Traditional computer vision techniques face challenges in addressing the advanced variability of skin lesion features, a gap partially bridged by convolutional neural networks (CNNs). To overcome the existing issues, we introduce an innovative convolutional ensemble network approach named deep autoencoder (DAE) with ResNet101. This method utilizes convolution-based deep neural networks for the detection of skin cancer. The ISIC-2018 public data taken from the source is used for experimental results, which demonstrate remarkable performance with the different in terms of performance metrics. The methods result in 96.03% of accuracy, 95.40 % of precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_14248 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging Dhibar, Sibasish Image and Video Processing Computer Vision and Pattern Recognition 68U10, 94A08 J.3; I.4.3; I.4.9 Skin cancer is a crucial health issue that requires timely detection for higher survival rates. Traditional computer vision techniques face challenges in addressing the advanced variability of skin lesion features, a gap partially bridged by convolutional neural networks (CNNs). To overcome the existing issues, we introduce an innovative convolutional ensemble network approach named deep autoencoder (DAE) with ResNet101. This method utilizes convolution-based deep neural networks for the detection of skin cancer. The ISIC-2018 public data taken from the source is used for experimental results, which demonstrate remarkable performance with the different in terms of performance metrics. The methods result in 96.03% of accuracy, 95.40 % of precision, 96.05% of recall, 0.9576 of F-measure, 0.98 of AUC. |
| title | ResNet101 and DAE for Enhance Quality and Classification Accuracy in Skin Cancer Imaging |
| topic | Image and Video Processing Computer Vision and Pattern Recognition 68U10, 94A08 J.3; I.4.3; I.4.9 |
| url | https://arxiv.org/abs/2403.14248 |