A robust method for skin cancer diagnosis based on interval analysis

• Main Authors: Haohai Zhang, Zhijun Wang, Liping Liang, Fatima Rashid Sheykhahmad
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Automatika
• Subjects: edge detection; skin cancer; computer-aided diagnosis; uncertainty; interval analysis; hukuhara difference; taylor inclusion functions; gaussian noise
• Online Access: http://dx.doi.org/10.1080/00051144.2020.1835108

Early diagnosis of skin cancer from dermoscopy images significantly reduces the mortality due to this cancer. However, several reasons impact the system diagnosis precision. One of the important problems in this process happens during image acquisition. Often, in medical photography, there are some uncertainties like noises and brightness variations, initial digitalization and sampling which affect the image quality. This study presents a new approach for border detection of the cancer area by considering the uncertainties. Interval analysis is utilized to extend the proposed edge detection method and the Hukuhara method is utilized for developing the differentiation formula for edge detection in the interval space. Simulation results are applied to two different skin cancer atlas and the results are compared with three popular methods by considering two types of noises including Gaussian noise and salt-and-pepper noise. The results showed that the introduced method gives better results than the compared methods.