Open AccessAssesment dermoscopy images of skin lesion using U-Net segmentation for clinicians teledermatology
Author(s) -
Adiratna Ciptaningrum,
I Ketut Eddy Purnama,
Reza Fuad Rachmadi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1175/1/012015
Subject(s) - segmentation , teledermatology , artificial intelligence , skin cancer , sørensen–dice coefficient , computer science , skin lesion , process (computing) , medicine , pattern recognition (psychology) , image segmentation , pathology , health care , cancer , telemedicine , economics , economic growth , operating system
Standard solutions to the process of assessing skin lesions are based on a medical pathology examination using dermoscopy images. Fortunately, abovementioned-mentioned diagnosis and treatment quite often show a defect-prone, yet in the hands of accomplished health care professionals. Due to the rapid development of skin cancer lesions, computational analysis is required. In order to minimize the probability of error, a segmentation task is required in which multiple medical analyzes can be conducted. These frameworks are generally required versatile and need assistance from advanced computing power. The proposed system is tested with datasets for dermoscopy images of clinical signs. U-Net segmentation method provides greater segmentation result IoU 94.37, Dice Coefficient 88.11, precision 90.87, recall (sensitivity) 91.82, accuracy 94.55, loss 16.8, and F1-score 91.34.