Author(s): Minakshi M. Sonawane, Bharti W. Gawali, Ramesh R. Manza, Sudhir Mendhekar

Email(s): minakshi919@gmail.com , drbhartirokade@gmail.com , manzaramesh@gmail.com , sudhir.medhekar@gmail.com

DOI: 10.52711/2349-2988.2022.00024   

Address: Minakshi M. Sonawane1, Bharti W. Gawali2, Ramesh R. Manza3, Sudhir Mendhekar4
1Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India.
2Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India.
3Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS), India.
4Department of Dermatology, Venereology and Leprosy Government Medical College, Aurangabad (MS), India.
*Corresponding Author

Published In:   Volume - 14,      Issue - 3,     Year - 2022


ABSTRACT:
Skin diseases are a serious health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of various data mining approaches, dermatological predictive classification has become increasingly predictive and accurate. It is more help to dermatologist to identify the disease, As a result, the development of machine learning approaches capable of efficiently. The purpose of this study is making an application of identification skin disease images by using the machines learning method, Support Vector Machine (SVM), and KNN techniques. The image processes and machine learning is performed early detection of skin diseases. The aim of this study is determined the classification of skin diseases in humans. Each skin disease has symptoms. It has five skin diseases such as Acne, Psoriasis, Wrath, Psoriasis, and Ulcer. We have collected 314 skin disease images from the government of hospital, Aurangabad with the help of mobile camera and Sony HD camera. Gaussian Filter is used for image pre-processing. The segmentation method is used for K-Means Clustering and the feature extraction method are used for feature extraction. We have used Haar feature, color feature, FCM, OS-FCM, GLCM and LBF features for classifications. Based on the result, the SVM is given 92% accuracy for haar feature, FCM and OS-FCM. and KNN classifier, K-Means are given 89% and 89% accuracy using mobile phone camera dataset. The SVM, KNN and K-Means are given 91%, 87% and 89% accuracy respectively using Sony HD camera dataset. SVM is given good result in both dataset.


Cite this article:
Minakshi M. Sonawane, Bharti W. Gawali, Ramesh R. Manza, Sudhir Mendhekar. Analysis of Skin disease techniques using Smart Phone and Digital Camera Identification of Skin Disease. Research Journal of Science and Technology. 2022; 14(3):145-5. doi: 10.52711/2349-2988.2022.00024

Cite(Electronic):
Minakshi M. Sonawane, Bharti W. Gawali, Ramesh R. Manza, Sudhir Mendhekar. Analysis of Skin disease techniques using Smart Phone and Digital Camera Identification of Skin Disease. Research Journal of Science and Technology. 2022; 14(3):145-5. doi: 10.52711/2349-2988.2022.00024   Available on: https://rjstonline.com/AbstractView.aspx?PID=2022-14-3-2


REFERENCES:
1.    R. J. Hay, N. E. Johns, H. C. Williams, I.W. Bolliger, R. P. Dellavalle, and D. J. Margolis, “The global burden of Skin disease in 2010: An Analysis of the prevalence and impact of skin conditions”, 55 J, Investigative Dermatology, vol. 134, no. 6, PP NO.1527_1534, 2014.
2.    Sardana K, Mahajan S, Sarkar R. Spectrum of skin diseases among Indianchildren. Pediatr Dermatol, 26 (1) pp no:6–13,2009.
3.    Palak Mehta, Bhumika Shah, “Review on Techniques and Steps of Computer Aided Skin Cancer Diagnosis” International Conference on Computational Modeling and Security (CMS2016). https://doi.org/10.1016/j.procs.2016.05.28.
4.    Online available: https://www.medicalnewstoday.com/articles/154322.
5.    Abraham Getachew Kelbore, Philip Owiti, Anthony J. Reid, Efa Ambaw Bogino, Lantesil Wondewo sen and Blen Kassahun Dessu, “Pattern of skin disease in childeren attending a dermatology clinic in a referral hospital in wolaita soda, southern Ethiopia”, BMC Dermatology, https://doi.org/10.1186/s12895-019-0085-5, pp no- 3-8, 2019.
6.    Housman TS, Feldman SR, Willi ford PM, Fleischer AB Jr., Goldman ND, et al., “Skin cancer is among the Most Costly of all Cancers to treat for the Medicare population”, J Am Acad Dermatol 48: pp. 425_429, 2003.
7.    Arifin, S., Kibria, G., Firoze, A., Amini, A., and Yan, H. (2012) “Dermatological Disease Diagnosis Using Color-Skin Images.” Xian: International Conference on Machine Learning and Cybernetics.
8.    Yasir, R., Rahman, A., and Ahmed, N. (2014) “Dermatological Disease Detection using Image Processing and Artificial Neural Network.“Dhaka: International Conference on Electrical and Computer Engineering.
9.    Santy, A., and Joseph, R. (2015) “Segmentation Methods for Computer-Aided Melanoma Detection.”Global Conference on Communication Technologies.
10.    Zeljkovic, V., Druzgalski, C., Bojic-Minic, S., Tameze, C., and Mayorga, P. (2015) “Supplemental Melanoma Diagnosis for Darker Skin Complexion Gradients.” Pan American Health Care Exchanges
11.    Suganya R. (2016) “An Automated Computer-Aided Diagnosis of Skin Lesions Detection and Classification for Dermoscopy Images.”
12.    Alam, N., Munia, T., Tavakolian, K., Vasefi, V., MacKinnon, N., and Fazel-Rezai, R. (2016) “Automatic Detection and Severity
13.    Kumar, V., Kumar, S., and Saboo, V. (2016) “Dermatological Disease Detection Using Image Processing and Machine Learning.” IEEE.
14.    A.A.L.C. Amarathunga, E.P.W.C. Ellawala, G.N. Abeysekara, C. R. J. Amalraj, “Expert System for Diagnosis of Skin Diseases”, International Journal of Scientific and Technology Research, IEEE, PP No-1-5, VOLUME 4, ISSUE 01, ISSN 2277-8616, January 2015.
15.    Krizhevsky, A., ILYA, S., and Geoffrey, E. (2012) “ImageNet Classification with Deep Convolutional Neural Networks.” Advances in Neural Information Processing Systems.
16.    Cristianini, N., Shawe, J., “Support Vector Machines”, 2000.
17.    SOMMERVILLE, I., “Software Engineering”. 9th .2011.
18.    Smits, Guido F., and Elizabeth M. Jordaan. "Improved SVM regression using mixtures of kernels." Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No. 02CH37290). Vol. 3. IEEE, 2002.
19.    Cesar Souza (2010, March 17) “http://crsouza.com/2010/03/17/kernelfunctions-for-machine-learning-applications.
20.    Fatima, Ruksar, Mohammed Zafar Ali Khan, and K. P. Dhruve. "Computer-aided multi-parameter extraction system to aid early detection of skin cancer melanoma." International Journal of Computer Science and Network Security 12.10 (2012): 74-86.

Recomonded Articles:

Author(s): A.K. Meena, Kiran Sharma, Vikas Jain, Bhavana Pal, Ajit K., Uttam Singh, R. Singh , M.M. Rao

DOI:         Access: Open Access Read More

Author(s): K. Parameswari , T. Ananthi

DOI:         Access: Open Access Read More

Author(s): Neela Ashish Kumar, Swarnalatha P, Prudhvi Chowdary, Jai Krishna Naidu, Kailasa Sandeep Kumar

DOI: 10.5958/2349-2988.2019.00016.0         Access: Open Access Read More

Author(s): S. Deepa, K. Kanimozhi , A. Panneerselvam

DOI:         Access: Open Access Read More

Author(s): Tanushree Chatterjee, Pradeep Kumar Sahu, Shilpi Chatterjee

DOI:         Access: Open Access Read More

Author(s): Shubhangi Dwivedi, Prashant Tiwari

DOI:         Access: Open Access Read More

Author(s): Shivani Sharma, R P Sharma

DOI:         Access: Open Access Read More

Author(s): M.R.Yadav, C.L. Dewangan

DOI:         Access: Open Access Read More

Author(s): Vidhi R. Patel, Dhrubo Jyoti Sen , C.N. Patel

DOI:         Access: Open Access Read More

Author(s): Girish Kapoor

DOI: 10.5958/2349-2988.2017.00009.2         Access: Open Access Read More

Author(s): Rathi HB, Bansal AK, Chauhan VKS

DOI:         Access: Open Access Read More

Research Journal of Science and Technology (RJST) is an international, peer-reviewed journal, devoted to science and technology...... Read more >>>

RNI: Not Available                     
DOI: 10.5958/2349-2988 


Recent Articles




Tags