Enhancing Skin Cancer Detection: A Comparative Study of Meta-Heuristic Optimized Convolutional Neural Networks
Year: 2025
Type: article
Abstract: CNNs are transforming into most common in a variety of fields, including medical imaging,and are generally acknowledged as an effective tool in the deep learning space. A critical usecase in this area is supporting professionals in the early detection of skin cancer bydermoscopy, which lowers death rates. Nonetheless, a number of variables may affect howaccurate system diagnostics are. Researchers have recently found computer-aided technologyto be an interesting field of study. In order to effectively diagnose skin cancer photos, thisresearch uses a state-of-the-art convolutional neural network (CNN) classifier that was tunedusing meta-heuristic approaches. Specifically built network models for visual datasets areused to train the classifier. There are several techniques to enhance the productivity of theneural network learning process. However, there is a dearth of research on the practical usesof deep learning-based neural networks. This study focuses on optimizing the weights andbiases in convolutional neural network (CNN) models by employing a novel approach thatutilizes the whale optimization algorithm. The effectiveness of this technique is evaluatedusing two skin cancer datasets, namely the DermQuest Database and the University ofWaterloo skin cancer database Dataset.
Authors: P Babu, T Meyyappan
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4409702908