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@article{173935,
        author = {Sunku Nithin Kumar and Dr.Bhavani Sankar Panda and Sadiya Shaik and Pradeep Yerninti and Shiswami Gudla},
        title = {Deploying Neural Network Models for Skin Cancer Recognition and Diagnosis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {1937-1943},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173935},
        abstract = {Skin cancer is a significant public health issue characterized by abnormal skin cell growth, primarily in areas exposed to UV radiation. The most common types include basal cell carcinoma, squamous cell carcinoma, and melanoma, with melanoma posing severe health risks if untreated. Timely detection and accurate classification are crucial for effective treatment. This study investigates the performance of various neural network architectures with datasets which contain diverse dermatoscopic images.We use models based on classification accuracy and computational efficiency. Our findings indicate that ResNet outperforms the other models in classification accuracy, while both ResNet and the custom CNN show faster testing times compared to the MLP. This research contributes valuable insights into different neural network approaches, advancing the field of skin cancer detection and enhancing diagnostic tools in dermatology},
        keywords = {Skin Cancer, Classification, Deep Learning, Dermoscopy,ResNet,CNN,MLP.},
        month = {March},
        }