Research Article |
An Improved Method for Skin Cancer Prediction Using Machine Learning Techniques
Author(s): Bharat Gupta1, Chakresh Kumar Jain2, Rishabh Lal Srivastava3, Debshishu Ghosh4 and Roshni Singh5
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 4, Special Issue on Complexity-BDA-ML
Publisher : FOREX Publication
Published : 30 October 2022
e-ISSN : 2347-470X
Page(s) : 881-887
Abstract
Among skin diseases the type that causes cancer are the fatal ones and pose the biggest issues. These issues arise since cancers are just much larger quantities of the same cells that are present around the body, which makes diagnosis very difficult until later stages. Now the onset of artificial intelligence and machine learning techniques, in the field of images, has allowed computers to identify sequences and patterns in images that can never be observed by the naked eye. Hence in order to battle skin cancer in its early stages a system has been proposed to identify and predict skin cancer in its earlier stages. A skin cancer prediction system has hence been created and implemented to predict three major types of skin cancer that affect humans. A dataset of the said skin cancer types and other types of skin diseases have been taken and analyzed. Apart from the model, a web application has been constructed for deployment on the web to enable the access of this model to the general masses. The current work is limited to selective dataset and model, which can be further extended.
Keywords: CNN
, Skin Cancer
, Cost-efficient
, Machine Learning
.
Bharat Gupta, Department of Computer Science and Engineering Jaypee Institute of Information Technology, Noida, India; Email: bharat.gupta@mail.jiit.ac.in
Chakresh Kumar Jain*, Department of Biotechnology Jaypee Institute of Information Technology, Noida, India; Email: ckj522@yahoo.com
Rishabh Lal Srivastava, Department of Computer Science and Engineering Jaypee Institute of Information Technology, Noida, India; Email: 19103088@gmail.com
Debshishu Ghosh, Department of Computer Science and Engineering Jaypee Institute of Information Technology, Noida, India; Email: 19103088@gmail.com
Roshni Singh, Department of Computer Science and Engineering Jaypee Institute of Information Technology, Noida, India; Email: 19103034@gmail.com
-
[1] Katherine Brind' Amour, All About Common Skin Disorders, https://www.healthline.com/health/skindisorders#prevention (accessed on: 09/05/2022 ) [Cross Ref]
-
[2] Gavin P. Dunn, Lloyd J. Old, Robert D. Schreiber, The Immunobiology of Cancer Immunosurveillance and Immunoediting, Immunity, Volume 21, Issue 2, 2004, Pages 137-148, ISSN 1074-7613, https://doi.org/10.1016/j.immuni.2004.07.017.[Cross Ref]
-
[3] Ames, B N et al. “The causes and prevention of cancer.” Proceedings of the National Academy of Sciences of the United States of America vol. 92, 12 (1995): 5258-65. doi:10.1073/pnas.92.12.5258 [Cross Ref]
-
[4] M. Vidya and M. V. Karki, "Skin Cancer Detection using Machine Learning Techniques," 2020 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2020, pp. 1-5, doi: 10.1109/CONECCT50063.2020.9198489.[Cross Ref]
-
[5] Seema Kolkur , D.R. Kalbande , V. Kharkar , “Machine Learning Approaches to Multi-Class Human Skin Disease Detection”, International Journal of Computational Intelligence Research (2018), http://ripublication.com/ijcir18/ijcirv14n1_03.pdf[Cross Ref]
-
[6] X. Dai, I. Spasić, B. Meyer, S. Chapman and F. Andres, "Machine Learning on Mobile: An On-device Inference App for Skin Cancer Detection," 2019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC), 2019, pp. 301-305, doi: 10.1109/FMEC.2019.8795362. [Cross Ref]
-
[7] Nawal Soliman ALKolifi ALEnezi, A Method of Skin Disease Detection Using Image Processing and Machine Learning, Procedia Computer Science, Volume 163, 2019, Pages 85-92, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2019.12.090[Cross Ref].
-
[8] Saja Salim Mohammed and Jamal Mustafa Al-Tuwaijari, “Skin Disease Classification System Based on Machine”, 2nd International Scientific Conference of Engineering Sciences (ISCES 2020), https://iopscience.iop.org/article/10.1088/1757- 899X/1076/1/012045/pdf [Cross Ref]
-
[9] Du-Harpur, X., Arthurs, C., Ganier, C., Woolf, R., Laftah, Z., Lakhan, M., Salam, A., Wan, B., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2021). Clinically Relevant Vulnerabilities of Deep Machine Learning Systems for Skin Cancer Diagnosis. The Journal of investigative dermatology, 141(4), 916–920. https://doi.org/10.1016/j.jid.2020.07.034 [Cross Ref]
-
[10] Sumit Saha, A Comprehensive Guide to Convolutional Neural Networks- the ELI5 way, 2018, https://towardsdatascience.com/a-comprehensive-guide-toconvolutional-neural-networks-the-eli5-way3bd2b1164a53,(accessed on 09/05/2022) [Cross Ref]
-
[11] E. Aarthi, S. Jana, W. Gracy Theresa, M. Krishnamurthy, A. S. Prakaash, C. Senthilkumar, S. Gopalakrishnan (2022), Detection and Classification of MRI Brain Tumors using S3-DRLSTM Based Deep Learning Model. IJEER 10(3), 597-603. DOI: 10.37391/IJEER.100331.[Cross Ref]
-
[12] Tschandl, Philipp, 2018, "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions", https://doi.org/10.7910/DVN/DBW86T, Harvard Dataverse, V[Cross Ref]
-
[13] Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A., Alraddadi, M. O., & Mahnashi, M. H. (2021). Skin Cancer Detection: A Review Using Deep Learning Techniques. International journal of environmental research and public health, 18(10), 5479. https://doi.org/10.3390/ijerph18105479[Cross Ref]
-
[14] Carr, S., Smith, C., & Wernberg, J. (2020). Epidemiology and risk factors of melanoma. Surgical Clinics, 100(1), 1-12.[Cross Ref]
-
[15] Yeh, I. (2020). New and evolving concepts of melanocytic nevi and melanocytomas. Modern Pathology, 33(1), 1-14.[Cross Ref]
-
[16] Peris, K., Fargnoli, M. C., Garbe, C., Kaufmann, R., Bastholt, L., Seguin, N. B., ... & European Association of Dermato-Oncology (EADO. (2019). Diagnosis and treatment of basal cell carcinoma: European consensus–based interdisciplinary guidelines. European Journal of cancer, 118, 10-34.[Cross Ref]
-
[17] de Oliveira, E. C., da Motta, V. R., Pantoja, P. C., Ilha, C. S. D. O., Magalhães, R. F., Galadari, H., & Leonardi, G. R. (2019). Actinic keratosis–review for clinical practice. International Journal of Dermatology, 58(4), 400-407.[Cross Ref]
-
[18] Cohen, P. R., Erickson, C. P., & Calame, A. (2019). Atrophic dermatofibroma: a comprehensive literature review. Dermatology and Therapy, 9(3), 449-468.[Cross Ref]
-
[19] Blum, A. G., Gillet, R., Athlani, L., Prestat, A., Zuily, S., Wahl, D., ... & Teixeira, P. G. (2021). CT angiography and MRI of hand vascular lesions: technical considerations and spectrum of imaging findings. Insights into Imaging, 12(1), 1-22.[Cross Ref]
-
[20] Nahata, H., & Singh, S. P. (2020). Deep learning solutions for skin cancer detection and diagnosis. In Machine Learning with Health Care Perspective (pp. 159-182). Springer, Cham.[Cross Ref]
-
[21] Daghrir, J., Tlig, L., Bouchouicha, M., & Sayadi, M. (2020, September). Melanoma skin cancer detection using deep learning and classical machine learning techniques: A hybrid approach. In 2020 5th international conference on advanced technologies for signal and image processing (ATSIP) (pp. 1-5). IEEE.[Cross Ref]
-
[22] Seem, A., Chauhan, A.K., Khan, R. (2022). Artificial Neural Network, Convolutional Neural Network Visualization, and Image Security. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_51[Cross Ref]
Bharat Gupta, Chakresh Kumar Jain, Rishabh Lal Srivastava, Debshishu Ghosh and Roshni Singh (2022), An Improved Method for Skin Cancer Prediction Using Machine Learning Techniques. IJEER 10(4), 881-887. DOI: 10.37391/IJEER.100422.