Skin Cancer Disease Detection Using MCD-GRU: A Deep Learning Approach
Md Basitur Rahman Bappi, S M Masfequier Rahman Swapno, M. M. Fazle Rabbi
Abstract
Cancers of the skin are particularly dangerous. Skin cancer may arise due to genetic abnormalities or mutations in DNA that are not correct. Deaths have skyrocketed due to a lack of awareness about warning indicators and prevention. Hence, timely identification is imperative to avoid the dissemination of cancer. Our goal in doing this ground-breaking study was to address the pressing problem of identifying different forms of skin cancer, such as benign, malignant, normal, and acne. We began the development of an advanced and unique deep learning model, Mix Conv Dense GRU (MCD-GRU), by utilizing a carefully selected dataset that included 3932 cases of skin cancer data. Regarding validation accuracy, the model obtained 99.90% and 99.12% in training. The MCD-GRU model performed exceptionally well during both the training and validation stages. Thorough testing was used to confirm this degree of precision, and the results showed a testing accuracy of 99.13%. This method shows the greatest skin cancer detection accuracy. This article explains how deep learning might enhance skin cancer detection. This innovative approach may improve health security and safety in the future.