Abstract: Skin cancer ranks among the most prevalent forms of cancer globally, contributing to millions of fatalities. Its primary cause lies in the uncontrolled mutation growth within DNA. Early detection is pivotal for enhancing treatment success rates. Modern medical practices leverage technology extensively, with intelligent systems aiding in the analysis and classification of skin conditions. Detecting infection rates accurately poses challenges due to the intricate texture of skin and the visual similarities of diseases. This study aims to analyze biomedical datasets containing pre-existing illness data to develop an effective method for distinguishing skin cancer as either malignant or benign. Leveraging ResNet-50 deep learning architectures, we classify the dataset. With a dataset comprising training images, our model achieves an accuracy of 86.66%. This accuracy may further improve with increased epochs.
Keywords: Skin Cancer, Intelligent-systems, Resnet-50, Malignant, Benign.
|
DOI:
10.17148/IJARCCE.2024.13452
