Author(s):
Rajarajeswari. S, J. Prassanna, Abdul Quadir Md, Christy Jackson J, Shivam Sharma, B. Rajesh
Email(s):
prassanna.j@vit.ac.in
DOI:
10.52711/0974-360X.2022.00758 
Address:
Rajarajeswari. S1, J. Prassanna1*, Abdul Quadir Md1, Christy Jackson J1, Shivam Sharma1, B. Rajesh2
1School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Chennai
2Department of Mathematics, University College of Engineering, Pattukkottai, 614701, India
*Corresponding Author
Published In:
Volume - 15,
Issue - 10,
Year - 2022
ABSTRACT:
Introduction: The identification and monitoring of benign moles and skin cancers leads to a challenging task because of the usual standard significant skin patches. Actually, the skin lesions vary very little in their look and only limited amount of information is available. There are seven fundamental types of skin cancer like Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) whereas Melanoma is the highly risky which has low survival rate. Objective: This work classifies skin lesions with the help of Convolution Neural Network and the images are trained end-to-end. A dataset comprised of 10000 clinical images were trained using Convolution Neural Network (CNN). Materials and Methods: The skin cancer identification process is generally separated into two basic components, image pre-processing which includes classification of images and removing the duplicate images and sharpening, which resizes the skin image. This work discusses a methodology to segment the high-level skin lesion and identification of malignancy more accurately with the help of deep learning: 1) Construction of a neural network, which detects the edge of a huge lesion accurately; 2) Designing model that can run on mobile phones. The model designed a transfer learning which is based deep on neural network and the fine turning that supports to attain high prediction accuracy. Results: The dataset comprises of a total of 10,000 images stored in two folders. The information about the data is stored in a data frame. Total 10000 dermoscopic images contains 374 melanoma images, 254 seborrheic keratosis images and 1372 nevus images. Using transfer learning validation loss, Top-2 accuracy and Top-3 accuracy have been calculated. The result has been compared with the different models. Conclusions: The proposed system can categorize healthy skin lesions, eczema, acne, malignant and benign skin lesions. The proposed work investigates the attributes acquired by the deep convolutional neural network. The attributes are extracted and the datasets were divided into seven different categories. Based on that categories the data was trained and validated. Based on the calculation the validation loss, top-2 accuracy, top-3 accuracy was calculated.
Cite this article:
Rajarajeswari. S, J. Prassanna, Abdul Quadir Md, Christy Jackson J, Shivam Sharma, B. Rajesh. Skin Cancer Detection using Deep Learning. Research Journal of Pharmacy and Technology2022; 15(10):4519-5. doi: 10.52711/0974-360X.2022.00758
Cite(Electronic):
Rajarajeswari. S, J. Prassanna, Abdul Quadir Md, Christy Jackson J, Shivam Sharma, B. Rajesh. Skin Cancer Detection using Deep Learning. Research Journal of Pharmacy and Technology2022; 15(10):4519-5. doi: 10.52711/0974-360X.2022.00758 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2022-15-10-31
REFERENCES:
1. Sonam S, Rekha B. Study of demographic profile of skin tumors in a tertiary care hospital. Int J Cur Res Rev. 2014; 06(16):24-28
2. Tavakolpour S, Daneshpazhooh M, Mahmoudi H. Skin cancer: Genetics, immunology, treatments, and psychological care. In: Cancer Genetics and Psychotherapy. Cham: Springer International Publishing; 2017. p. 851–934. doi: 10.1007/978-3-319-64550-6_18
3. Maglogiannis I, Doukas CN. Overview of advanced computer vision systems for skin lesions characterization. IEEE Trans Inf Technol Biomed. 2009;13(5):721–33. doi: 10.1109/TITB.2009.2017529.
4. Dai X, Spasic I, Meyer B, Chapman S, Andres F. Machine learning on mobile: An on-device inference app for skin cancer detection. In: 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE; 2019. doi: 10.1109/FMEC.2019.8795362
5. Janda M, Youl PH, Lowe JB, Elwood M, Ring IT, Aitken JF. Attitudes and intentions in relation to skin checks for early signs of skin cancer. Prev Med. 2004;39(1):11–8. doi: 10.1016/j.ypmed.2004.02.019
6. Hoffmann K, Gambichler T, Rick A, Kreutz M, Anschuetz M, Grünendick T, et al. Diagnostic and neural analysis of skin cancer (DANAOS). A multicentre study for collection and computer-aided analysis of data from pigmented skin lesions using digital dermoscopy. Br J Dermatol. 2003;149(4):801–9. doi: 10.1046/j.1365-2133.2003.05547.x
7. Sharon, J. Jenifa, and L. Jani Anbarasi. "Diagnosis of DCM and HCM heart diseases using neural network function." International Journal of Applied Engineering Research 13.10 (2018): 8664-8668. doi: 10.1007/978-981-16-1244-2_7
8. Prassanna J. Rahim R. Bagyalakshmi K. Manikandan R. Patan R. Effective Use of Deep Learning and Image Processing for Cancer Diagnosis. Kose U., Alzubi J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer. 2021 doi.org:10.1007/978-981-15-6321-8_9
9. Sharon, J. Jenifa, L. Jani Anbarasi, and Benson Edwin Raj. "DPSO-FCM based segmentation and Classification of DCM and HCM Heart Diseases." 2018 Fifth HCT Information Technology Trends (ITT). IEEE, 2018. doi: 10.1109/CTIT.2018.8649511.
10. Jawahar, Malathy, et al. "Diagnosis of covid-19 using optimized pca based local binary pattern features." International Journal of Current Research and Review 13.6 Special Issue (2021). doi:http://dx.doi.org/10.31782/IJCRR.2021.SP171
11. Jawahar, Malathy, et al. "Diabetic Foot Ulcer Segmentation using Color Space Models." 2020 5th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2020. doi: 10.1109/ICCES48766.2020.9138024
12. J. Prassanna, Jani L. Anbarasi, R. Prabhakaran, Kanchana Devi V, Rajiv Vincent, R. Manikandan, Ambeshwar Kumar2 ,"Enhanced Safe and Secure Distributed Healthcare System Through Efficient Duplication Detection Approach." Int J Cur Res Rev| Vol 12.24 (2020): 101. doi: 10.31782/IJCRR.2020.122407
13. Modigari Narendra, L. Jani Anbarasi, S. Graceline Jasmine, J. Prassanna and R. Prabhakaran, “Breast Cancer Detection Using Histology Images: A Survey”, Jour of Adv Research in Dynamical & Control Systems, Vol. 12, 07-Special Issue, 2020. doi: 10.5373/JARDCS/V12SP7/20202140.
14. Reghukumar, Arathi, et al. "Vision Based Segmentation and Classification of Cracks Using Deep Neural Networks." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29.Supp01 (2021): 141-156. doi:10.1142/S0218488521400080
15. Kawahara J, Hamarneh G. Multi-resolution-tract CNN with hybrid pretrained and skin-lesion trained layers. In: Machine Learning in Medical Imaging. Cham: Springer International Publishing; 2016. p. 164–71. doi: 10.1007/978-3-319-47157-0_20
16. Sunil G, Vinod M, Pr R. Exfoliative toxin mediated staphylococcal scalded skin syndrome: A review. Int J Curr Res Rev. 2020;12(22):86–90. doi:10.31782/IJCRR.2020.122212
17. Ech-Cherif A, Misbhauddin M, Ech-Cherif M. Deep neural network based mobile dermoscopy application for triaging skin cancer detection. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). IEEE; 2019. doi: 10.1109/CAIS.2019.8769517
18. Chiem A, Al-Jumaily A, Khushaba RN. A novel hybrid system for skin lesion detection. In: 2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information. IEEE; 2007.doi:10.1109/ISSNIP.2007.4496905
19. Lau HT, Al-Jumaily A. Automatically early detection of skin cancer: Study based on nueral netwok classification. In: 2009 International Conference of Soft Computing and Pattern Recognition. IEEE; 2009.doi: 10.1109/SoCPaR.2009.80
20. Kalra M, Kumar S. Various image enhancement techniques for skin cancer detection using mobile app. In: 2015 International Conference on Computer, Communication and Control (IC4). IEEE; 2015.doi: 10.1109/IC4.2015.7375681
21. Skin Cancer Facts & Statistics, accessed 10 January 2021, < https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/>
22. Nasr-Esfahani E, Samavi S, Karimi N, Soroushmehr SMR, Jafari MH, Ward K, et al. Melanoma detection by analysis of clinical images using convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc. 2016;2016:1373–6.doi: 10.1109/EMBC.2016.7590963
23. TensorFlow (TF) to CoreML Converter [Accessed: 5 Feb.2019]. Available from: https://github.com/tf-coreml/tfcoreml.
24. Nirali A, Shah S, Prajapati S, Hansa G. Histomorphological spectrum of skin adnexal tumors at a tertiary care hospital - a retrospective study. Int J Cur Res Rev. 2016; 08(04):13-18. doi: ijmsph.2020.02036202011032020