Automated Skin Cancer Detection Using Advanced Deep Learning Approach
Keywords:
Deep Learning, Skin Cancer, Melanoma, Convolutional Neural Network, DermoscopyAbstract
Skin cancer, specifically melanoma, is a health issue of growing concern globally, during which early identification optimizes the preferred treatment available. Manual inspections are often error-prone and subjective, yet the primary method of diagnostic examination relies mostly on visual assessment. Automated systems have tremendous capability to help dermatologists diagnose cancerous lesions accurately and efficiently because of advancements in medical imaging and deep learning. This paper presents an automated skin cancer detection system that uses techniques from deep learning, utilizing Convolutional Neural Networks (CNN) to separate cancerous skin lesions from benign ones. An architecture based on CNN is constructed to interpret dermoscopic photos, in which preprocessing procedures are performed, such as the transformation of the RGB color space and application of the Local Binary Pattern (LBP) to enhance feature extraction. Also, an optimized version of VGG-16 is implemented for classification with improved accuracy compared to previously discussed methods. Benchmark datasets, including HAM10000 and ISIC, have undergone testing, revealing that the model demonstrates state-of-the-art results by achieving accuracies exceeding 97%, surpassing all other models. The results indicate that the incorporation of deep learning in skin cancer detection can speed up diagnosis, alleviate the burden on dermatologists, and greatly improve early detection, which could translate to improved patient outcomes.
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