Author(s): Karthiga M, Priyadarshini R K, Bazila Banu A

Email(s): karthigam@bitsathy.ac.in

DOI: 10.5958/0974-360X.2020.00230.9   

Address: Karthiga M1, Priyadarshini R K2, Bazila Banu A3
Dept. of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, India.
*Corresponding Author

Published In:   Volume - 13,      Issue - 3,     Year - 2020


ABSTRACT:
Computer based analysis has become a most important part in medical industry nowadays. Skin cancer has become a most dreadful disorders and it is can be diagnosed using computer based technique. Dermoscopy images can be used for training the classifier for correct prediction of melanoma. A novel methodology based on deep convolution neural network is utilized for absolute diagnosis of malevolent melanoma. Transfer learning technique is employed along with deep convolution neural network based Inception v3 framework. The outcomes are obtained by utilizing the proposed methodology with a total of 2700 dermoscopic images. Maximum rate of accuracy, sensitivity and specificity are obtained from the proposed implementation. The proposed results outperform the results for classification of skin lesions by dermatologists.


Cite this article:
Karthiga M, Priyadarshini R K, Bazila Banu A. Malevolent Melanoma diagnosis using Deep Convolution Neural Network. Research J. Pharm. and Tech 2020; 13(3):1248-1252. doi: 10.5958/0974-360X.2020.00230.9

Cite(Electronic):
Karthiga M, Priyadarshini R K, Bazila Banu A. Malevolent Melanoma diagnosis using Deep Convolution Neural Network. Research J. Pharm. and Tech 2020; 13(3):1248-1252. doi: 10.5958/0974-360X.2020.00230.9   Available on: https://rjptonline.org/AbstractView.aspx?PID=2020-13-3-35


REFERENCES:

1.       Nami N, Giannini E, Burroni M, Fimiani M, Rubegni P. Teledermatology: state-of-the-art and future perspectives. Expert Review of Dermatology. 2012 1;7(1):1-3.

2.      Fabbrocini G, Triassi M, Mauriello MC, Torre G, Annunziata MC, De Vita V, Pastore F, D’Arco V, Monfrecola G. Epidemiology of skin cancer: role of some environmental factors. Cancers. 2010;2(4):1980-1989.

3.      Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115.

4.      Ali AR, Deserno TM. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. InMedical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment 2012 ; 8318 - 83181I)

5.      Kittler H, Pehamberger H, Wolff K, Binder M. Diagnostic accuracy of dermoscopy. The lancet oncology. 20020;3(3):159-65.

6.      Ali AR, Deserno TM. A systematic review of automated melanoma detection in dermatoscopic images and its ground truth data. InMedical Imaging 2012: Image Perception, Observer Performance, and Technology Assessment 2012 ; 8318 -  83181.

7.      Fabbrocini G, De Vita V, Pastore F, D'Arco V, Mazzella C, Annunziata MC, Cacciapuoti S, Mauriello MC, Monfrecola A. Teledermatology: from prevention to diagnosis of nonmelanoma and melanoma skin cancer. International journal of telemedicine and applications. 2011

8.      Menegola A, Fornaciali M, Pires R, Bittencourt FV, Avila S, Valle E. Knowledge transfer for melanoma screening with deep learning. In2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) 2017 ; 18: 297-300.

9.      Pomponiu V, Nejati H, Cheung NM. Deepmole: Deep neural networks for skin mole lesion classification. In2016 IEEE International Conference on Image Processing (ICIP) 2016 ;25: 2623-2627.

10.   Codella N, Cai J, Abedini M, Garnavi R, Halpern A, Smith JR. Deep learning, sparse coding, and SVM for melanoma recognition in dermoscopy images. InInternational workshop on machine learning in medical imaging 2015 Oct 5 (pp. 118-126). Springer, Cham.

11.   Kawahara J, BenTaieb A, Hamarneh G. Deep features to classify skin lesions. In2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) 2016 : 1397-1400.

12.   Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115.

13.   Han SS, Kim MS, Lim W, Park GH, Park I, Chang SE. Classification of the clinical images for benign and malignant cutaneous tumors using a deep learning algorithm. Journal of Investigative Dermatology. 2018 1;138(7):1529-38.

14.   Bi L, Kim J, Ahn E, Feng D. Automatic skin lesion analysis using large-scale dermoscopy images and deep residual networks. arXiv preprint arXiv:1703.04197. 2017 :1-2.

15.   Kawahara J, Hamarneh G. Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. InInternational Workshop on Machine Learning in Medical Imaging 2016 : 164-171.

16.   Navarrete-Dechent C, Dusza SW, Liopyris K, Marghoob AA, Halpern AC, Marchetti MA. Automated dermatological diagnosis: hype or reality?. The Journal of investigative dermatology. 2018;138(10):2277.

17.   Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging. 2016 11;35(5):1285-98.

18.   Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S. CNN features off-the-shelf: an astounding baseline for recognition. InProceedings of the IEEE conference on computer vision and pattern recognition workshops :. 806-813.

19.   Zhou B, Lapedriza A, Xiao J, Torralba A, Oliva A. Learning deep features for scene recognition using places database. InAdvances in neural information processing systems 2014 : 487-495.

20.   Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 : 2818-2826.

21.   Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. Going deeper with convolutions. InProceedings of the IEEE conference on computer vision and pattern recognition 2015 : 1-9).

22.   Lin M, Chen Q, Yan S. Network in network. arXiv preprint arXiv:1312.4400. 2013 16.

23. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-resnet and the impact of residual connections on learning. InThirty-First AAAI Conference on Artificial Intelligence 2017 12.

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