Skin Cancer Detection using Machine Learning Techniques: A Review

Mansi Mishra*, R. K. Khare**
*-** Department of Computer Science and Engineering, Shri Shankaracharya Technical Campus, Chhattisgarh, India.
Periodicity:July - September'2022

Abstract

Skin cancer is one of the most common types of cancer worldwide, accounting for about one-third of all diagnoses. Unrepaired DNA breaks in skin cells, which result in genetic flaws or mutations on the skin, are the primary cause of skin cancer. Early detection of skin cancer signs is necessary due to the rising incidence of cases, high death rate, and expensive medical treatments. Researchers have created a number of early detection methods for skin cancer because of how dangerous these problems are. Skin cancer is detected and benign skin cancer from melanoma is distinguished using lesion features including symmetry, color, size, form, etc. Skin lesions are organized in layers, and dermatologists take this into account when making a diagnosis. CNN outperformed even board-certified dermatologists. Machine-assisted methods for detecting cancer are also more efficient. Deep learning is an artificial intelligence operation that simulates the work of the human brain in organizing data and designing decision-making patterns. This research gives a thorough analysis of deep learning methods for skin cancer early detection.

Keywords

Deep Learning, Deep Neural Network (DNN), Machine Learning, Melanoma, Support Vector Machine (SVM), Skin Lesion.

How to Cite this Article?

Mishra, M., and Khare, R. K. (2022). Skin Cancer Detection using Machine Learning Techniques: A Review. i-manager’s Journal on Image Processing, 9(3), 34-40.

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