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This study integrates a comprehensive literature survey with the development and preliminary evaluation of a deep learning methodology for melanoma skin cancer detection. Our approach includes collecting a diverse dataset of skin cancer images, which undergoes rigorous preprocessing to enhance image quality essential for effective model training using TensorFlow and Keras. The models, built on advanced neural network architectures, demonstrate initial promise in accuracy, precision, recall, and F1-score metrics, underscoring potential improvements. We also prepare for the integration of this system into a web-based application designed to aid dermatologists in diagnosis, highlighting the synergy between detailed data analysis and practical medical application. This research underscores the critical role of precise data handling and advanced modeling techniques in enhancing diagnostic processes for skin cancer.

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Synergizing Literature Insights and Deep Learning for Effective Skin Cancer Detection and Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Radhika Takkella,
  • Pavan Kumar Pagadala

摘要

This study integrates a comprehensive literature survey with the development and preliminary evaluation of a deep learning methodology for melanoma skin cancer detection. Our approach includes collecting a diverse dataset of skin cancer images, which undergoes rigorous preprocessing to enhance image quality essential for effective model training using TensorFlow and Keras. The models, built on advanced neural network architectures, demonstrate initial promise in accuracy, precision, recall, and F1-score metrics, underscoring potential improvements. We also prepare for the integration of this system into a web-based application designed to aid dermatologists in diagnosis, highlighting the synergy between detailed data analysis and practical medical application. This research underscores the critical role of precise data handling and advanced modeling techniques in enhancing diagnostic processes for skin cancer.