A Comparative Study of Various Transfer Learning Models on Skin Cancer Confirmation Methods - Mehmet Ali Altuncu; Kaplan Kaplan; Melih Kuncan (Journal of Universal Computer Science)
Skin cancer confirmation is critical in determining a patient’s treatment planning process after diagnosis. A proper confirmation process enables the determination of the type, stage, and other characteristics of skin cancer, helping to plan the appropriate treatment. These methods prevent the progression of the disease, thereby contributing to a better response to treatment and improving the patient's quality of life. Dermoscopic images are commonly used to confirm skin cancer types. To obtain meaningful results from these images, researchers often apply artificial intelligence techniques in such studies. Specifically, transfer learning models have been commonly used to enhance the features of these images due to the limited availability of medical image data and the difficulty in extracting meaningful information from such data. While most studies focus on classifying skin cancer types, this research aims to classify skin cancer confirmation types using dermoscopic dataset images. Dermoscopic HAM10000 dataset images were used for this purpose. The dataset includes four different confirmation methods: confocal, consensus, follow-up, and histopathology. Four distinct transfer learning models—Resnet-50, Resnet-101, VGG19, and InceptionResnetV2—were utilized. Additionally, ensemble learning was conducted based on the results of these models using the maximum voting approach. The highest success rate was achieved with Resnet-101 at 96.04%. Considering the comparative results, the accuracy of our promising model proved to be significantly high.