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@article{173961,
        author = {Aditi Shankar and Alaina Umme Hafiza},
        title = {Skin Cancer Detection and Classification using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {10},
        pages = {2244-2251},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=173961},
        abstract = {Skin cancer, particularly melanoma, remains a leading cause of death worldwide, with early detection crucial for improving survival rates. The development of machine learning-based diagnostic tools, particularly Convolutional Neural Networks (CNNs), offers significant potential to assist in non-invasive skin cancer detection. This study explores the integration of CNNs in building an intelligent skin cancer detection system capable of real-time image analysis and user feedback. A mobile application is proposed to offer users the ability to upload or capture images of suspicious skin lesions for immediate analysis. The system’s core components include image preprocessing, feature extraction, and classification using machine learning models. Additionally, evaluation metrics such as accuracy, precision, recall and F1 score are used to assess model performance.},
        keywords = {CNN, Classification, Detection, TensorFlow, Keras, Skin Cancer},
        month = {March},
        }