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ISSN: 2321-9653
Estd : 2013

Ijraset Journal For Research in Applied Science and Engineering Technology

Machine Learning Based System for the Detection of Skin Cancer

Authors: Avni Sharma

DOI Link: https://doi.org/10.22214/ijraset.2025.73033

Certificate: View Certificate

Abstract

Skin cancer is the increasing common cancer throughout the world nowadays,and occurrence rates are increasing very rapidly. If the cancer is predicted at early stage then the victim life can be saved .If detected at early stage then the patient can be treated successfully.An expert system can be buildup to detect the skin cancer.By such system many lives can be saved. This paper provides the concise review of various medical expert systems and the best methods used by various researchers to detect the skin cancer.

Introduction

Skin cancer is the most common cancer worldwide and is rapidly increasing. It is mainly categorized into melanoma and non-melanoma types. Skin cancer involves abnormal growth of skin tissues, which can be benign (non-harmful) or malignant (harmful). Benign growths contain melanin in the outer epidermis layer, but when melanin penetrates deeper into the dermis, it becomes dangerous. Direct sunlight exposure is a key cause of skin cancer. Cancer cells can spread from their original site to other body parts via the bloodstream, a process called metastasis. The epidermis consists of three layers: an upper layer, a middle layer of squamous cells, and a bottom layer made of melanocytes and basal cells.

Conclusion

This review paper gives the comparison the methods used for detection of skin cancer. The comparison in the table is based on the methods , dataset used and performance measures like accuracy. The comparison in the table is done using the literature survey. There are number of techniques for segmentation of skin cancer detection but the methods having highest performance measure are k-means clustering, Otsu method and active contour. In future studies, these three segmentation techniques may be used with different parameters.

References

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Paper Id : IJRASET73033

Publish Date : 2025-07-06

ISSN : 2321-9653

Publisher Name : IJRASET

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