| Autor: |
Ogundokun RO; Department of Computer Science, Landmark University, Omu Aran 251103, Nigeria.; Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania., Li A; School of Marxism, Guangzhou University of Chinese Medicine, Guangzhou 510006, China., Babatunde RS; Department of Computer Science, Kwara State University, Malete 241103, Nigeria., Umezuruike C; Department of Software Engineering, Bowen University, Iwo 232102, Nigeria., Sadiku PO; Department of Computer Science, University of Ilorin, Ilorin 240003, Nigeria., Abdulahi AT; Department of Computer Science, Kwara State Polytechnic, Ilorin 240211, Nigeria., Babatunde AN; Department of Computer Science, Kwara State University, Malete 241103, Nigeria. |
| Abstrakt: |
One of the most promising research initiatives in the healthcare field is focused on the rising incidence of skin cancer worldwide and improving early discovery methods for the disease. The most significant factor in the fatalities caused by skin cancer is the late identification of the disease. The likelihood of human survival may be significantly improved by performing an early diagnosis followed by appropriate therapy. It is not a simple process to extract the elements from the photographs of the tumors that may be used for the prospective identification of skin cancer. Several deep learning models are widely used to extract efficient features for a skin cancer diagnosis; nevertheless, the literature demonstrates that there is still room for additional improvements in various performance metrics. This study proposes a hybrid deep convolutional neural network architecture for identifying skin cancer by adding two main heuristics. These include Xception and MobileNetV2 models. Data augmentation was introduced to balance the dataset, and the transfer learning technique was utilized to resolve the challenges of the absence of labeled datasets. It has been detected that the suggested method of employing Xception in conjunction with MobileNetV2 attains the most excellent performance, particularly concerning the dataset that was evaluated: specifically, it produced 97.56% accuracy, 97.00% area under the curve, 100% sensitivity, 93.33% precision, 96.55% F1 score, and 0.0370 false favorable rates. This research has implications for clinical practice and public health, offering a valuable tool for dermatologists and healthcare professionals in their fight against skin cancer. |
