Abstract
Melanoma is a type of carcinoma with a notably high mortality rate. Accurate diagnosis of this aggressive cancer is crucial due to its severe implications. Key diagnostic indicators include asymmetrical shape, heterogeneous color, diameter greater than 6 mm, and irregular borders, which dermatologists typically identify through visual examination. The conventional method for carcinoma detection is biopsy, involving the removal or scraping of skin samples for extensive laboratory testing. This process is both painful and time- consuming. To improve patient experience and enhance diagnostic efficiency, computer-based detection using image processing techniques and deep learning algorithms, specifically Convolutional Neural Networks (CNNs), has been developed to accurately identify melanoma. Keywords: Deep learning, CNN, Computer- based detection
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: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
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.
