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@article{170035,
        author = {Vattikutti Mounika and Ch.Papa Rao},
        title = {A Comparative Study of CNN Architectures for Multi-Label Skin Cancer Classification},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3016-3018},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170035},
        abstract = {Skin cancer remains one of the most prevalent and life-threatening diseases, exacerbated by a general lack of awareness regarding its signs and preventive measures. As the global burden of skin cancer continues to rise, early detection is crucial for reducing mortality rates. This paper presents a novel approach for the detection and classification of multi-label skin cancer using Convolutional Neural Networks (CNNs) and advanced image processing techniques. We preprocess the HAM10000 dataset to eliminate irrelevant features and standardize input data, enhancing the model's performance. A comprehensive experimental analysis was conducted on the HAM10000 dataset, which encompasses seven distinct types of skin cancer. The results demonstrate that our CNN-based model outperforms traditional machine learning classifiers such as SVM, Decision Trees (DT), and Gaussian Naive Bayes (GNB), achieving superior accuracy in multi-label classification tasks. This work underscores the potential of deep learning techniques in the early diagnosis of skin cancer, paving the way for more effective clinical interventions.},
        keywords = {Skin cancer, Convolutional Neural Networks, Classification, HAM10000, Multi-label classification},
        month = {November},
        }