Deep Learning Enhanced Skin Cancer Analysis: Advancements in Melanoma Detection with Medical Support
Keywords:
Skin cancer, Deep learning, Fine-tuning, VGG-16, Inception V3, Inception ResNet V2, DenseNet201, Ensemble learning, Web based model.Abstract
The identification and classification of skin cancer lesions are crucial due to their profound health implications. The paper presents web application based a deep learning model to address the skin cancer detection. This proposed system aimed to fine-tune well-established models for skin lesion classification. Ensemble learning techniques were applied by combining thoroughly fine-tuned Inception V3 and DenseNet201 models to improve classification accuracy. In this web based model development, the front end model is developed to provide user interface for skin cancer classification using Python, JavaScript, HTML, CSS and Flask framework. The proposed scheme is compared to other standard models such as VGG-16, Inception V3, Inception ResNet V2, and DenseNet201. In the experimental part, the proposed scheme achieves higher accuracy rate of 88.5% than compared to Baseline CNN model with 75.64 %, VGG-16 with 79.65 %, Inception ResNet V2 with 82.50 %, with HAM10000 dataset, showcasing the efficacy of our methodology in precisely categorizing skin cancer lesions.
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