Paper Key : IRJ************755
Author: Dasari Kundana,Komara Swathi,Polaki Shanmukha Rao,Kalivarapu Kartheek,Pedada Kameswara Rao
Date Published: 05 Apr 2024
Abstract
"Melanoma, which constitutes 76% of all skin cancer-related deaths, underscores the critical necessity of early detection to enhance treatment efficacy. In response, a sophisticated Convolutional Neural Network (CNN) model has been meticulously developed for precise melanoma identification. Utilizing the power of deep learning, this model adeptly examines images, proving to be an essential asset for dermatologists in their diagnostic endeavors. By automating detection processes, it streamlines workflows, poised to redefine melanoma diagnosis paradigms.The swift analysis of images, coupled with real-time alerts, empowers medical practitioners, promising improved early detection rates. Timely intervention is paramount in melanoma cases, significantly impacting patient prognosis. This groundbreaking solution alleviates the burden of manual assessments, enabling dermatologists to direct their expertise towards critical cases. The integration of this CNN-based model into clinical settings holds immense potential to save lives by expediting diagnosis and initiating treatment for melanoma patients. Moreover, this approach ensures superior accuracy even with limited input data, promising enhanced outcomes in melanoma management."
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