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| 1. Verfasser: | |
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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2025
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.14500/aro.12024 |
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Inhaltsangabe:
- <p>Skin cancer is considered one of the most common and dangerous diseases in the world because so many people do not <br>pay attention to it. In addition, skin cancer is a medical condition that a doctor cannot accurately diagnose from imaging data during a manual examination. Therefore, there is a great need to apply deep learning methods for early detection of skin cancer, as these methods are excellent in the field of medical image processing. This paper presents a deep learning model based on the convolutional neural network algorithm to provide automatic detection of skin cancer. The model basically consists of two scenarios: binary classification (benign and malignant) of the data set without an image segmentation process and binary classification of the same data set after applying four image segmentation methods (threshold based segmentation, edge-based segmentation, binary fill holes technique, and removing small objects). The input images in the first scenario are three channels and one channel in the second scenario. These image segmentation techniques have significantly improved the accuracy of the proposed model, as the proposed model achieved 92.18% before applying segmentation and 96.83% after applying image segmentation.</p>