Abstract
Abstract: Contrary to inspection with the untrained eye, Dermatoscopy is a frequently used diagnostic procedure that enhances the identification of benign and malignant pigmented skin diseases. To train artificial neural networks to automatically identify pigmented skin lesions, dermatoscopic images are also a good source. An artificial neural network was previously trained to successfully distinguish melanocytic nevi from melanomas, the deadliest kind of skin cancer. This was done using dermatoscopic pictures. Although the results were encouraging, the study suffered from a limited sample size and a lack of dermatoscopic pictures besides melanoma or nevi, like most prior investigations. Recent improvements in graphics card power and machine learning methods have boosted hopes for the availability of automated diagnostic systems that can quickly identify all types of pigmented skin lesions without the assistance of a human specialist. A lot of annotated pictures are needed for the training of neural network-based diagnosis algorithms, however the quantity of high-quality dermatoscopic images with accurate diagnoses is small or restricted to a small number of disease classes.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Research in Applied Science and Engineering Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
