Enhancing Dermatological Diagnosis with Machine Learning and Image Processing: A Skin Cancer Detection Study

Authors : Isha Salunkhe; Jermin Shaikh; Dipika Harshad Mankar

Volume/Issue : Volume 8 - 2023, Issue 9 - September

Google Scholar : https://tinyurl.com/57z3pxnx

Scribd : https://tinyurl.com/3m3jmzdb

DOI : https://doi.org/10.5281/zenodo.8426043

Previous research articles have covered several methods used for identifying and categorizing malignancies of the skin, including image pre-processing, picture division, extraction of features, and classification. Skin illness is the most frequent human disease in general. Cancer is a group of disorders that may manifest itself practically everywhere in the body. Cancer is, at its most basic, a disease of the genes in our body's cells. Detecting dangerous skin disorders, particularly cancer, demands the identification of pigmented lesions on the skin. Image detection approaches and computer categorization abilities can help enhance skin cancer diagnosis accuracy. Skin cancer is one of the most common and potentially fatal types of cancer in the globe. A timely and correct diagnosis is critical for optimal therapy and patient outcomes. This work proposes a unique method to dermatological diagnostics based on the integration of machine learning and image processing techniques for the early identification of skin cancer. The fundamental goal of this research is to create a dependable and efficient skin cancer detection system that can aid dermatologists and other healthcare professionals in making appropriate diagnostic judgments. To train and test our machine learning models, we use a varied collection of skin lesion photos including a wide spectrum of benign and malignant instances.

Keywords : Melanoma, Support Vector Machine, CNN, Skin Lesion, Machine Learning.


Paper Submission Last Date
31 - December - 2023

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