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Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers

A. Murugan*, S. Anu H. Nair**, K. P. Sanal Kumar***

* Department of Computer Science Engineering, Thiru Kolanjiappar Govt. Arts College, Vridhachalam, Tamil Nadu, India.

** Department of Computer Science Engineering, Annamalai University, Chidambaram (Deputed to WPT), Tamil Nadu, India.

*** Department of Computer Science Engineering, R.V. Government Arts College, Chengalpattu, Tamil Nadu, India.

Periodicity:June - August'2019
DOI : https://doi.org/10.26634/jpr.6.2.16761

Abstract

In today’s advanced world, skin cancer is the most common cause of death amongst humans. Skin cancer is the abnormal development of skin cells. It develops on the body always exposed to daylight, however it can happen anywhere on the body too. The greater part of the skin cancers is they are curable at early stages. So an early and fast detection of skin cancer can save a patient's life. Skin cancer (Melanoma) is one among the most deadly cancers. Using the improvement of image preprocessing in noise removal, the images of skin disease are obtained. The images are then segmented using K-means clustering technique. Segmented images are input to the feature extraction phase and the extracted images are classified by using classification techniques like Naive Bayes and PNN.

Keywords

Melanoma, Skin Cancer, Diagnosis, Human Faults.

How to Cite this Article?

Murugan, A., Nair, S. A. H., & Kumar, K. P. S. (2019). Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers. i-manager’s Journal on Pattern Recognition, 6(2), 25-31. https://doi.org/10.26634/jpr.6.2.16761

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