Machine Learning and Deep Learning Integration for Skin Diseases Prediction

Machine Learning and Deep Learning Integration for Skin Diseases Prediction
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-2
Year of Publication : 2022
Authors : Samir Kumar Bandyopadhyay, Payal Bose, Amiya Bhaumik, Sandeep Poddar
DOI :  10.14445/22315381/IJETT-V70I2P202

How to Cite?

Samir Kumar Bandyopadhyay, Payal Bose, Amiya Bhaumik, Sandeep Poddar, "Machine Learning and Deep Learning Integration for Skin Diseases Prediction," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 13-21, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I2P202

Abstract
Living creature skin disease is a fairly prevalent ailment. In the medical world, monitoring dermatological disorders and classifying them is a complex process. Due to the sheer intricacy of individual skin tone and the visible proximity effect of infections, recognizing the precise type can be challenging at times. As a result, it is critical to diagnose and recognize skin disease as soon as possible. Artificial intelligence (AI) is quickly expanding in therapeutic areas in a modern context. For diagnostic purposes, much deep learning (DL) and machine learning (ML) methods are applied. These strategies drastically enhance the diagnosing process while also speeding it up. In this study, to improve disease detection, a model combining deep learning (DL) and machine learning (ML) has been developed. For classification, three sets of machine learning models were utilized, and for feature selection, four sets of pre-trained deep learning models were being used. For classification models, deep neural networks Alexnet, Googlenet, Resnet50, and VGG16 were used, while Support Vector Machine, Decision tree, and Ensemble boosting Adaboost classifier were applied for classification. To identify the best prediction model, a comparative study was carried out. The hybrid method Resnet50 with SVM produced the best results, with 99.11% accuracy.

Keywords
Deep Feature Extraction, Deep Learning (DL), Machine Learning (ML), Skin Diseases Detection, Support Vector Machine (SVM).

Reference
[1] Mohammed, S. S., & Al-Tuwaijari, J. M. Skin Disease Classification System Based on Machine Learning Technique: A Survey. IOP Conference Series: Materials Science and Engineering, 1076 (1) (2021) 012045.
[2] Hashmani, M. A., Jameel, S. M., Rizvi, S. S. H., & Shukla, S. An adaptive federated machine learning-based intelligent system for skin disease detection: A step toward an intelligent dermoscopy device. Applied Sciences (Switzerland), 11(5) (2021) 1–19.
[3] Li, H., Pan, Y., Zhao, J., & Zhang, L. Skin disease diagnosis with deep learning: a review. Hongfeng Li. (2020).
[4] Dildar, M., Akram, S., Irfan, M., Khan, H. U., Ramzan, M., Mahmood, A. R., Alsaiari, S. A., Saeed, A. H. M., Alraddadi, M. O., & Mahnashi, M. H. Skin cancer detection: A review using deep learning techniques. International Journal of Environmental Research and Public Health. 18(10) (2021).
[5] Verma, A. K., Pal, S., & Kumar, S. Comparison of skin disease prediction by feature selection using ensemble data mining techniques. Informatics in Medicine Unlocked. 16(April) (2019) 100202.
[6] Jaychandra Reddy, V., & Nagalakshmi, T. J. Skin disease detection using artificial neural network. Indian Journal of Public Health Research and Development, 10(11) (2019) 3829–3832.
[7] Alkolifi Alenezi, N. S. A Method of Skin Disease Detection Using Image Processing and Machine Learning. Procedia Computer Science. 163 (2019) 85–92.
[8] Santhiya, D. S., Pravallika, S. S. L., Sukrutha, M. A., Nishanth, I., Iswarya, N., & Aishwarya, D. Skin Disease Detection using V2 and V3 in Machine Learning. International Journal of Engineering Science and Computing. 9(4) (2019) 21343–21347.
[9] Bhadula, S., Sharma, S., Juyal, P., & Kulshrestha, C. Machine Learning Algorithms based Skin Disease Detection. International Journal of Innovative Technology and Exploring Engineering. 9(2) (2019) 4044–4049.
[10] Warsi, F., Khanam, R., Kamya, S., & Suárez-Araujo, C. P. An efficient 3D colour-texture feature and neural network technique for melanoma detection. Informatics in Medicine Unlocked, 17(November 2018) (2019) 100176.
[11] Leelavathy S, Jaichandran R, Shobana R, Vasudevan, S. S. P. and N. Skin Disease Detection Using Computer Vision and Machine Learning Technique. European Journal of Molecular & Clinical Medicine. 7(4) (2020) 2999–3003.
[12] Gouda, N., & Amudha, J. Skin Cancer Classification using ResNet. 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020. (2020) 536–541.
[13] Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors. 21(8) (2021) 2852.
[14] Banasode, P., Patil, M., & Ammanagi, N. A Melanoma Skin Cancer Detection Using Machine Learning Technique: Support Vector Machine. IOP Conference Series: Materials Science and Engineering. 1065(1) (2021) 0–5.
[15] Kadampur, M. A., & Al Riyaee, S. X. Skin cancer detection: Applying a deep learning-based model-driven architecture in the cloud for classifying dermal cell images. Informatics in Medicine Unlocked, 18(December 2019) (2021) 100282.
[16] Hasan, M., Barman, S. Das, Islam, S., & Reza, A. W. Skin cancer detection using convolutional neural network. ACM International Conference Proceeding Series: March 2020 (2019) 254–258.
[17] Gopalakrishnan, S., Abishek. B, Dr.E., Vijayalakshmi, Dr A., Rajendran, Dr V. Analysis And Diagnosis Using Deep-Learning Algorithm On Erythemato-Squamous Disease. International Journal of Engineering Trends and Technology 69(3) (2021) 52-57.
[18] Mohan, N. Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm. International Journal of Engineering Trends and Technology. 68(9) (2020) 109-120.
[19] Patel, K. Image Feature Extraction: Traditional and Deep Learning Techniques. Medium (2020).
[20] Deep Learning Based Image Segmentation with AlexNet Feature Extraction for Classification of Mammogram Images. International Journal of Pharmaceutical Research 13(01) (2021).
[21] Alake, R. Deep Learning: GoogLeNet Explained - Towards Data Science. Medium. (2020).
[22] Kaushik, A. Understanding ResNet50 architecture. OpenGenus IQ: Computing Expertise & Legacy (2020).
[23] Manasa, K., & Student, M. T Skin Cancer Detection Using VGG-16. European Journal of Molecular & Clinical Medicine 08(01) (2021) 1419–1426.
[24] Waseem, M. How To Implement Classification In Machine Learning? Edureka (2021).
[25] Team, E. What is the Definition of Machine Learning? Expert.Ai (2021)
[26] Ray, S. SVM | Support Vector Machine Algorithm in Machine Learning. Analytics Vidhya (2021).
[27] Great Learning Team. The Ultimate Guide to AdaBoost Algorithm | What is AdaBoost Algorithm? GreatLearning Blog: Free Resources What Matters to Shape Your Career! (2021).
[28] Narayanan A G, H., Singh, Dr J.A.P. Skin Disease Ensemble Classification Using Transfer Learning and Voting Classifier. International Journal of Engineering Trends and Technology. 69(12) (2021) 287-293.
[29] Saini, A. Decision Tree Algorithm - A Complete Guide. Analytics Vidhya (2021).
[30] ISIC Archive. (2016). ISIC Archive. Retrieved October 19, 2021, from https://www.isic-archive.com/