Early Detection of Skin Cancer with Mobile Application Using Artificial Intelligence Techniques

Abstract

Cancer is a significant health problem due to the increase in patients worldwide and causing death. Recently, it has been seen that skin cancer is among the most common types of cancer. Prolonging the survival of individuals struggling with skin cancer and reducing treatment costs is possible with early diagnosis. However, techniques used for early diagnosis in today's health systems have limitations such as requiring extensive human resources, long-term results, and not having easy access to these services for everyone. For this reason, systems that are easy to apply, produce accurate results in the context of scientific methods and are accessible to everyone are needed for early skin cancer detection. Early detection of skin cancer is possible with the use of artificial intelligence techniques. This study aims to classify benign and malignant skin images and inform users of the results via mobile application. CNN, KNN and Decision Tree algorithms were used to classify the images. “Experiments ISIC: Skin Cancer: Malignant vs. It was carried out by applying the augmentation technique on the “Benign” data set. As a result of the experiments, the most successful results were obtained with the Transfer Learning algorithm, 94.89%. The study also compared the data and results obtained with other architectures. Experimental results show that it is possible to detect skin cancer early with artificial intelligence techniques and to notify the user of the results with a mobile application. We believe the study's results will shed light on new research for early skin cancer detection.