Computer Vision-Based Skin Cancer Detection
DOI:
https://doi.org/10.5281/ijurd.v1i2.67Keywords:
Skin Cancer, Computer Vision, Dermoscopic Images, CNN, Medical DiagnosisAbstract
Skin cancer is one of the most common forms of cancer worldwide, and early detection plays a crucial role in improving patient survival rates. This paper presents a Computer Vision-Based Skin Cancer Detection framework using deep learning techniques for automated analysis of dermoscopic images. The proposed system utilizes Convolutional Neural Networks to extract visual features and classify skin lesions into benign and malignant categories. Image preprocessing and augmentation techniques are applied to enhance model performance and generalization. The framework also incorporates transfer learning strategies using pre-trained models to improve accuracy while reducing training time and data requirements. Experimental results demonstrate that the proposed approach achieves high classification accuracy and reliability, comparable to expert-level diagnosis. Additionally, integration with prior research in machine learning and healthcare analytics enhances system robustness and adaptability. The study highlights the potential of computer vision techniques in enabling fast, accurate, and cost-effective skin cancer screening, particularly in settings with limited access to dermatological expertise.
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Copyright (c) 2025 Shweta Roy, Rani Desai
This work is licensed under a Creative Commons Attribution 4.0 International License.
