Skin Disease Classification Using CNN Algorithms
DOI:
https://doi.org/10.4108/eetpht.9.4039Keywords:
Skin disease classification, Transfer learning, Deep learning, Medical images, Clinical decision support systemAbstract
INTRODUCTION: Dermatological disorders, particularly human skin diseases, have become more common in recent decades. Environmental factors, socioeconomic problems, a lack of a balanced diet, and other variables have all contributed to an increase in skin diseases in recent years. Skin diseases can cause psychological suffering in addition to physical injury, especially in people with scarred or disfigured faces.
OBJECTIVES: The use of artificial intelligence or computer-based technologies in the detection of face skin disorders has advanced dramatically over time. Even for highly experienced doctors and dermatologists, identifying skin disorders can be tricky since many skin diseases have a visual affinity with the surrounding skin and lesions.
METHODS: Today, the majority of skincare specialists rely on time-consuming, traditional methods to identify disorders. Even though several research have demonstrated promising results on the picture classification job, few studies compare well-known deep learning models with various metrics for categorizing human skin disorders.
RESULTS: This study examines and contrasts various skin illnesses in terms of cosmetics and common skin concerns. Our dataset includes over 25000 of the eight most common skin disorders. Convolutional neural networks have shown imaging performance that is comparable to or greater than that of humans. We used 11 different network algorithms to identify the illnesses in the sample and compared the results.
CONCLUSION: To adjust the format of incoming photographs, we do certain image pre-processing and image scaling for each model. ResNet152 beat other deep learning methods in terms of recall, accuracy, and precision on a test dataset of 1930 images.
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S. Akyeramfo-Sam, A. addo Philip, Derrick Yeboah, Nancy Nartey, et. al., "A Web-Based Skin Disease Diagnosis Using Convolutional Neural Networks," I. J. Information Technology and Computer Science, vol. 11, pp. 54-60, 2019. DOI: https://doi.org/10.5815/ijitcs.2019.11.06
D. A. Shoieb, S. M. Youssef, and W, M. Aly, "ComputerAided Model for Skin Diagnosis Using Deep Learning," Journal of Image and Graphics, vol. 4, no. 2, 2016. DOI: https://doi.org/10.18178/joig.4.2.122-129
S. Kumar Patnaik, M, Singh Sidhu, Yaagyanika Gehlot, Bhairvi Sharma, and P Muthu*, "Automated Skin Disease Identification using Deep Learning Algorithm," Biomedical & Pharmacology Journal, vol. 11(3), pp. 1429-1436, 2018. DOI: https://doi.org/10.13005/bpj/1507
R. Yasir, Md. Ashiqur Rahman, and Nova Ahmed, "Dermatological Disease Detection Using Image Processing and Artificial Neural Network," in 8th International Conference on Electrical and Computer Engineering 20-22 December 2014, IEEE 2014, Dhaka, Bangladesh, 2014. DOI: https://doi.org/10.1109/ICECE.2014.7026918
P. Bose, S.K. Bandyopadhyay, A. Bhaumik, and Dr. Sandeep Poddar, "Skin Disease Detection: Machine Learning vs Deep Learning," Preprints, 13 September 2021. DOI: https://doi.org/10.20944/preprints202109.0209.v1
Jyothilakshmi K. K and Jeeva J. B, "Detection of malignant skin diseases based on the lesion segmentation," 2014 International Conference on Communication and Signal Processing, 2014, pp. 382-386, doi: 10.1109/ICCSP.2014.6949867. DOI: https://doi.org/10.1109/ICCSP.2014.6949867
P. R. Hegde, M. M. Shenoy, and B. H. Shekar, "Comparison of Machine Learning Algorithms for Skin Disease Classification Using Color and Texture Features," 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2018, pp. 1825-1828, doi: 10.1109/ICACCI.2018.8554512. DOI: https://doi.org/10.1109/ICACCI.2018.8554512
S. Kolkur and D. R. Kalbande, "Survey of texture based feature extraction for skin disease detection," 2016 International Conference on ICT in Business Industry & Government (ICTBIG), 2016, pp. 1-6, doi: 10.1109/ICTBIG.2016.7892649. DOI: https://doi.org/10.1109/ICTBIG.2016.7892649
Aditya, W., Luthfil Hakim, N., Shih, T. K., Enkhbat, A., & Thaipisutikul, T.. IC4Windows–hand gesture for controlling MS windows. 2020 - 5th International Conference on Information Technology (InCIT), 2020, 2. DOI: https://doi.org/10.1109/InCIT50588.2020.9310967
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S.. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 2–5. DOI: https://doi.org/10.1038/nature21056
K. M., M. A. Kassem, and M. M. Foaud, "Skin Cancer Classification using Deep Learning and Transfer Learning Hosny," 2018 9th Cairo International Biomedical Engineering Conference (CIBEC), Cairo, Egypt, 2018, pp. 90-93, doi: 10.1109/CIBEC.2018.8641762. DOI: https://doi.org/10.1109/CIBEC.2018.8641762
A. Romero Lopez, X. Giro-i-Nieto, J. Burdick and O. Marques, "Skin lesion classification from dermoscopic images using deep learning techniques," 2017 13th IASTED International Conference on Biomedical Engineering (BioMed), Innsbruck, Austria, 2017, pp. 49-54, doi: 10.2316/P.2017.852-053. DOI: https://doi.org/10.2316/P.2017.852-053
M. A. Kassem, K. M. Hosny, and M. M. Fouad, "Skin Lesions Classification Into Eight Classes for ISIC 2019 Using Deep Convolutional Neural Network and Transfer Learning," in IEEE Access, vol. 8, pp. 114822- 114832, 2020, doi: 10.1109/ACCESS.2020.3003890. DOI: https://doi.org/10.1109/ACCESS.2020.3003890
Bhadoria R.K., Biswas S. (2020) A Model for Classification of Skin Disease Using Pretrained Convolutional Neural Network. In: Mandal J., Mukhopadhyay S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_14. DOI: https://doi.org/10.1007/978-981-15-2188-1_14
Md. Aminur Rab Ratul, Mohammad Hamed Mozaffari, Enea Parimbelli, WonSook Lee, "Atrous Convolution with Transfer Learning for Skin Lesions Classification”.
J. Velasco, C. Pascion, J, Wilmar Alberio, Jonathan Apuang, John Stephen Cruz, Mark Angelo Gomez, Benjamin Jr. Molina, Lyndon Tuala, August Thio-ac, and Romeo Jr. Jorda, "A Smartphone-Based Skin Disease Classification Using MobileNet CNN," International Journal of Advanced Trends in Computer Science and Engineering, vol. 8, 2019. DOI: https://doi.org/10.30534/ijatcse/2019/116852019
K.l Polat, and K. Onur Koc, "Detection of the skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All," Journal of Artificial Intelligence and Systems, vol. 2, pp. 80-97, 10 February 2020. DOI: https://doi.org/10.33969/AIS.2020.21006
K. Sujay Rao, P.Suresh Yelkar, O. Narayan Pise, and Dr. Swapna Borde, "Skin Disease Detection using Machine Learning," [1] Kritika Sujay Rao, Pooja Suresh Yelkar, Omkar Narayan Pise, and Dr. Swapna Borde, "Skin Disease Detection using Machine Learning", International Journal of Engineering Research & Technology (IJERT) ISSN: 2278- 0181 Published by, www.ijert.org, vol. 9, no. 3, 2021. DOI: https://doi.org/10.17577/IJERTV9IS030384
C.Yu Zhu, Yu-Kun Wang, Hai-Peng Chen, Kun-Lun Gao, Chang Shu, Jun-Cheng Wang, Li-Feng Yan, Yi-Guang Yang, Feng-Ying Xie, and Jie Liu, "A Deep Learning Based Framework for Diagnosing Multiple Skin Diseases in a Clinical Environment," Frontiers in Medicine, vol. 8, 16 April 2021. DOI: https://doi.org/10.3389/fmed.2021.626369
V. Rawat, D. P. Singh, N. Singh, P. Kumar and T. Goyal, "A Comparative Study of various Skin Cancer using Deep Learning Techniques," 2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), Greater Noida, India, 2022, pp. 505-511, doi: 10.1109/CISES54857.2022.9844409. DOI: https://doi.org/10.1109/CISES54857.2022.9844409
I. K. Pious and R. Srinivasan, "A Review on Early Diagnosis of Skin Cancer Detection Using Deep Learning Techniques," 2022 International Conference on Computer, Power and Communications (ICCPC), Chennai, India, 2022, pp. 247-253, doi: 10.1109/ICCPC55978.2022.10072274. DOI: https://doi.org/10.1109/ICCPC55978.2022.10072274
T. R. Anik, P. Talukder, I. Faruki, I. S. Ibn Rahman and E. Hossain, "Analysis of Automated Skin Disease Classification Exploiting Different Machine Learning Techniques," 2023 IEEE 13th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 2023, pp. 1145-1151, doi: 10.1109/CCWC57344.2023.10099354. DOI: https://doi.org/10.1109/CCWC57344.2023.10099354
X. Xiang and T. Chen, "Skin Disease Classification Using Inception-ResNetV2 and Data Augmentation," 2022 14th International Conference on Computer Research and Development (ICCRD), Shenzhen, China, 2022, pp. 42-47, doi: 10.1109/ICCRD54409.2022.9730242. DOI: https://doi.org/10.1109/ICCRD54409.2022.9730242
Dataset link: https://www.kaggle.com/datasets/bhanuprasanna/isic-2019
Lakshmanaprabu, S. K., Mohanty, S. N., Shankar, K., Arunkumar, N., & Ramirez, G. (2019). Optimal deep learning model for classification of lung cancer on CT images. Future Generation Computer Systems, 92, 374-382. DOI: https://doi.org/10.1016/j.future.2018.10.009
Agarwal, R., Suthar, J., Panda, S. K., & Mohanty, S. N. (2023). Fuzzy and Machine Learning based Multi-Criteria Decision Making for Selecting Electronics Product. EAI Endorsed Transactions on Scalable Information Systems, 10(5). https://doi.org/10.4108/eetsis.3353 DOI: https://doi.org/10.4108/eetsis.3353
Lakshmanaprabu, S. K., Mohanty, S. N., Krishnamoorthy, S., Uthayakumar, J., & Shankar, K. (2019). Online clinical decision support system using optimal deep neural networks. Applied Soft Computing, 81, 105487. DOI: https://doi.org/10.1016/j.asoc.2019.105487
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