چکیده مقاله
An early and accurate diagnosis of skin cancer is critical for effective treatment and improved patient outcomes In this paper, we propose and evaluate two lightweight convolutional neural network CNN architectures for skin lesion classification using the PH2 dataset The first model is a simple CNN with three convolutional blocks, while the second is a Mini ResNet inspired CNN with enhanced feature extraction capability Both models aim to balance classification accuracy and computational efficiency, enabling deployment on resource limited devices Experimental results demonstrate that the Mini ResNet model achieves 92% accuracy, while the simple CNN achieves 88% Our findings suggest that these lightweight architectures are promising candidates for real time skin cancer screening applications
کلیدواژهها
نویسندگان
Amin Mashayekhi Shams
Electrical Engineering Department, Engineering Faculty, University of Zanjan, Zanjan, Iran
Sepideh Jabbari
Electrical Engineering Department, Engineering Faculty, University of Zanjan, Zanjan, Iran
شیوه ارجاع
Mashayekhi Shams, Amin and Jabbari, Sepideh,1404,A Comparative Study of Lightweight Convolutional Neural Networks for Skin Cancer Classification,The Second International Congress of Cancer,Zanjan
ارائهشده در
مجموعه مقالات دومین کنگره بین المللی پیشگیری از سرطان8 مهر 1404 · زنجان