The results demonstrate that deep learning techniques can significantly assist in early detection and classification of skin cancer, thereby supporting dermatologists in clinical decision-making and improving diagnostic efficiency and mortality rates associated with skin cancer.
Experimental results indicate that the proposed framework achieves high diagnostic accuracy while providing interpretable insights that assist dermatologists in understanding model predictions.
Choudhuri Saswat Pattnaik, Priyanka Shit, Subhashree Raul et al.· International Research Journ...· 0 citations
The results show that both the CNN architectures used for the experiment and the automated classification of skin lesions using them are effective, and the MobileNetV2 performs the best in the experimental framework of this study.
Raees Adnan, Fawad Nasim, Muqaddas Salahuddin· SOCIAL PRISM· 0 citations
This paper presents a comprehensive deep learning-based framework for the automated detection and classification of skin diseases from dermoscopic and clinical images, built on the ResNet50 convolutional neural network architecture and evaluated using standard classification metrics.
Nisha Rajodiya, Shailendra Mishra, Sumitra Menaria et al.· International Journal of Res...· 0 citations
The proposed system uses Convolutional Neural Networks (CNNs) and related deep learning techniques to automatically learn discriminative visual features from skin-lesion images and classify disease categories to demonstrate the potential of artificial intelligence in dermatology.
S. Behera, Sri Bapuji Bismaya Kumar Giri, Subham Singh Mundari et al.· International Research Journ...· 0 citations
Results demonstrate that CNN-based architectures achieve robust performance in capturing local spatial features, while transformer-based models provide competitive results through global contextual representation, however, variations are observed in model calibration and false-negative rates, which are critical for cli...
Melanoma is a type of skin cancer that is one of the most aggressive, and its diagnosis requires quick and accurate identification to increase patient survival. This paper draws a comparative analysis of advanced deep learning systems, namely, VGG and traditional CNN systems, used in automatic melanoma detection in der...
A. S.· International Conference on...· 0 citations
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