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@article{178751,
        author = {Abhaykumar Baral and Prof.Yashwant Ingle},
        title = {Automated  Detection Of Skin Cancer Using Image Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {5921-5927},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=178751},
        abstract = {Skin cancer is one of the most prevalent types of cancer in the world, and improved survival rates and efficient treatment depend on early detection. In this exploration, we propose an automated system for skin cancer discovery using deep literacy- grounded image bracket. The approach leverages transfer literacy with threepre-trained Convolutional Neural Network (CNN) infrastructures VGG16, ResNet50, and GoogLeNet. These models were trained and estimated on the HAM10000 dataset, a large and different collection of dermatoscopic images of skin lesions. Each model was fine- tuned to classify images into multiple skin complaint orders. The performance of the models was anatomized grounded on criteria similar as delicacy, loss, and visualizations like confusion matrices and training angles. Among the tested models, ResNet50 demonstrated the loftiest delicacy, followed nearly by GoogLeNet and VGG16. The results show that deep literacy, particularly transfer literacy with CNNs, can effectively support early skin cancer opinion, potentially aiding dermatologists in clinical decision- timber.},
        keywords = {Skin Cancer Discovery, Deep literacy, Image Bracket, Convolutional Neural Networks (CNN), Transfer literacy, ResNet50, VGG16, GoogLeNet, HAM10000 Dataset, Medical Image Analysis},
        month = {May},
        }