International Journal on Science and Technology

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An Advanced Deep Residual Learning Framework for Accurate Skin Cancer Detection Using ResNet152

Author(s) Shruti Chouhan, Prof. Pankaj Raghuwanshi, Neha Khare
Country India
Abstract Skin cancer is one of the most rapidly increasing and life-threatening diseases worldwide, where early and accurate diagnosis plays a critical role in improving patient survival rates. Traditional diagnostic approaches heavily depend on dermatologist expertise and manual examination, which may lead to inconsistent results and delayed detection. To overcome these limitations, this research proposes an advanced deep learning-based framework for automated skin cancer detection using the ResNet152 architecture. The proposed system utilizes dermoscopic skin lesion images from the ISIC dataset and applies preprocessing techniques such as image resizing, normalization, noise removal, and data augmentation to improve model performance and generalization capability. The ResNet152 model is employed for deep feature extraction and binary classification of skin lesions into benign and malignant categories. The proposed framework is evaluated using standard performance metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that the proposed ResNet152 model achieves superior performance with 97% accuracy, 98% precision, 97% recall, and 98% F1-score compared to existing models such as ResNet50 and ResNet101. The findings confirm that deep residual learning significantly improves feature extraction, classification reliability, and automated diagnosis capability for skin cancer detection. The proposed framework can support clinical decision-making and contribute to intelligent healthcare systems for early skin cancer diagnosis.
Keywords Skin Cancer Detection, Deep Learning, ResNet152, Dermoscopic Images, Convolutional Neural Network, Deep Residual Learning, Medical Image Analysis, Artificial Intelligence, Melanoma Classification, Transfer Learning.
Published In Volume 17, Issue 2, April-June 2026
Published On 2026-06-14
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