Skin Cancer Detection Using Machine Learning

Skin Cancer Detection Using Machine Learning

Maridu.Bhargavi1, Assistant Professor, Department of CSE,

Vignan’s Foundation for Science, Technology & Research, Vadlamudi, Guntur Dt., Andhra Pradesh.

V.Ujwala2, M. Hema Sree3, Krishna priya4

2,3,4 UG Students, Department of CSE,
Vignan’s Foundation for Science, Technology & Research, Vadlamudi, Guntur Dt., Andhra Pradesh

ABSTRACT

skin cancer is a normal form of cancer that affects millions of people worldwide. Early diagnosis and detection are crucial in the successful treatment of skin cancer. Machine learning algorithms have been utilized in recent years to assist in the detection and diagnosis of skin cancer. This abstract presents a unique approach to skin cancer detection using machine learning. The suggested approach combines machine learning algorithms with image processing methods to analyse images of skin lesions and classify them among 9 different classes. The system employs an innovative feature selection method to identify the most significant features that aid in the classification process. These features include Edge detection, histogram, Texture analysis. The classification of images is done by using Decision Tree, KNN, Random Forest and Voting classifier algorithms. The proposed system was trained and tested on a dataset of skin lesion images obtained from various sources, including dermatology clinics and online databases. The findings show that the suggested strategy is highly accurate at identifying and categorising skin lesions. The unique strategy described in this abstract may increase the precision and effectiveness of skin cancer detection, resulting in earlier diagnoses and more effective therapies.

Keywords: Decision Tree, KNN, Voting classifier, Random Forest, GLCM