i-manager Publications

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

References

[1]. Ahmed, I. N., & Chaya, P. (2014). Segmentation and classification of skin cancer images. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5),1349-1353.

[2]. Ali, A. R. A., & Deserno, T. M. (2012, February). A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. In Medical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment (Vol. 8318, p. 83181I). International Society for Optics and Photonics. https://doi.org/10.1117/12.912389

[3]. Barata, C., Ruela, M., Francisco, M., Mendonça, T., & Marques, J. S. (2013). Two systems for the detection of melanomas in dermoscopy images using texture and color features. IEEE Systems Journal, 8(3), 965-979. https://doi.org/10.1109/JSYST.2013.2271540

[4]. Hoshyar, A. N., Al-Jumaily, A., & Sulaiman, R. (2011, June). Review on automatic early skin cancer detection. In 2011 International Conference on Computer Science and Service System (CSSS) (pp. 4036-4039). IEEE. https://doi.org/10.1109/CSSS.2011.5974581

[5]. Indira, D. N. V. S. L. S., & Suprya, P. J. (2011). Detection & analysis of skin cancer using wavelet techniques. International Journal of Computer Science and Information Technologies, 2(5), 1927-1932.

[6]. Jadhav, S. R., Kamat, D. K. (2014). Segmentation based detection of skin cancer. IRF International Conference.

[7]. Jaleel, J. A., Salim, S., & Aswin, R. B. (2013, March). Computer aided detection of skin cancer. In 2013 International Conference on Circuits, Power and Computing Technologies (ICCPCT) (pp. 1137-1142). IEEE. https://doi.org/10.1109/ICCPCT.2013.6528879

[8]. Mahmoud, M. K. A., Al-Jumaily, A., & Takruri, M. (2011, December). The automatic identification of melanoma by wavelet and curvelet analysis: Study based on neural network classification. In 2011 11^th International Conference on Hybrid Intelligent Systems (HIS) (pp. 680- 685). IEEE. https://doi.org/10.1109/HIS.2011.6122188

[9]. Ramya, V. J., Navarajan, J., Prathipa, R., & Kumar, L. A. (2015). Detection of melanoma skin cancer using digital camera images. ARPN Journal of Engineering and Applied Sciences, 10(7), 3082-3085.

[10]. Ruiz, D., Berenguer, V., Soriano, A., & SáNchez, B. (2011). A decision support system for the diagnosis of melanoma: A comparative approach. Expert Systems with Applications, 38(12), 15217-15223. https://doi.org/ 10.1016/j.eswa.2011.05.079

[11]. Sadeghi, M., Razmara, M., Lee, T. K., & Atkins, M. S. (2011). A novel method for detection of pigment network in dermoscopic images using graphs. Computerized Medical Imaging and Graphics, 35(2), 137-143. https://doi.org/10.1016/j.compmedimag.2010.07.002

[12]. Salah, B., Alshraideh, M., Beidas, R., & Hayajneh, F. (2011). Skin cancer recognition by using a neuro-fuzzy system. Cancer Informatics, 10, CIN-S5950.

[13]. Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3(1), 109-118. https://doi.org/10.1016 /0893-6080(90)90049-Q

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	35	35	200	15
Pdf & Online	35	35	400	25

Skin Cancer Diagnosis Using Naive Bayes and PNN Classifiers

Abstract

Keywords

How to Cite this Article?

References

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