Cilt Kanseri Teşhisi için Konvolüsyonel Sinir Ağları Tabanlı Bilgisayar Destekli Tanıda (CNN-CAD) Dijital Görüntü Kalitesinin İyileştirilmesi

Abstract

The practice of detecting skin cancer is based primarily on a visual examination by a dermatologist, followed by a series of tests for a more accurate diagnosis. The concept "the earlier cancer is detected in its natural history, the more effective the treatment is likely to be" is also valid for skin cancer. Hence, any delayed or missed diagnosis can lead to a more severe clinical stage or, what's worse, death. On the other hand, the lack of biomarkers in clinical use brings about overdiagnosis and unnecessary biopsies. DL-CAD system seems to be an excellent candidate for improving diagnostic accuracy and reducing unnecessary treatments. However, the vast majority of conventional CADs manipulate dermoscopic images, which require not only costly equipment but also time-consuming processing. Despite the difficulties with precision, state-of-the-art DL-CAD systems provide an interpretation using digital images, requiring no expertise in cost-effective dermoscopic image capture and interpretation. Preprocessing methods play a crucial role in solving this problem. This study presents results with regard to pre-processing steps to improve the images to be used in the diagnosis of the 5 most common skin cancer types for the proposed CNN based ResNet50 deep learning model. To the best of our knowledge it is the first time that ResNet50 deep-learning model has been utilized in diagnosis of skin cancer.