Enhancing Skin Disease Diagnosis Through Fine-Tune Convolutional Neural Network: A Comparative Study with Multi-class Approach

Najnin Akter Ringky; Abu Kowshir Bitto; Khalid Been Md. Badruzzaman Biplob; Md. Fazla Elahe; Musabbir Hasan Sammak; Tapushe Rabaya Toma

doi:10.5614/itbj.ict.res.appl.2023.18.2.4

Authors

Najnin Akter Ringky Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,
Abu Kowshir Bitto Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,
Khalid Been Md. Badruzzaman Biplob Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,
Md. Fazla Elahe Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,
Musabbir Hasan Sammak Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,
Tapushe Rabaya Toma Department of Software Engineering, Faculty of Science and Technology, Daffodil International University, Daffodil Smart City, Birulia, Savar, Dhaka-1216,

DOI:

https://doi.org/10.5614/itbj.ict.res.appl.2023.18.2.4

Keywords:

fine-tune, imbalance data, medical imaging, skin disease, Xception

Abstract

Due to their similar appearance, skin disorders frequently disguise their early warning signs from our skin, which is the defense system of the body. Preventing serious disorders requires their early detection. This work investigated the use of fine-tune transfer learning as a fast and accurate way to diagnose skin diseases. To classify different skin issues, we used pre-trained models, i.e., InceptionV3, DenseNet201, and Xception. This work examined 17,500 photos from three sources. It was found that fine-tune Xception performed exceptionally well, with an accuracy rate of 99.14%. It was closely followed by DenseNet201 and InceptionV3, each with different processing speeds, 98.74% and 98.46%, respectively. We used transfer learning with data sets validated by medical experts, outperforming earlier research in precision. This more accurate detection of skin diseases could greatly improve patient outcomes and expedite medical procedures. This approach is new in that it fine-tunes transfer learning by utilizing a vast number of data to increase accuracy compared to other researcher works.

Downloads

Download data is not yet available.

References

Hosny, K.M., Kassem, M.A. & Foaud, M.M., Classification of Skin Lesions using Transfer Learning and Augmentation with Alex-Net, PloS One, 14(5), e0217293, May, 2019.

Bitto, A.K. & Mahmud, I., Multi Categorical of Common Eye Disease Detect using Convolutional Neural Network: A Transfer Learning Approach, Bulletin of Electrical Engineering and Informatics, 11(4), pp.2378-2387, 2022.

Hosny, K.M., Kassem, M.A. & Foaud, M.M., Skin Melanoma Classification using ROI and Data Augmentation with Deep Convolutional Neural Networks, Multimedia Tools and Applications, 79, pp.24029-24055, June, 2020.

El Gannour, O., Hamida, S., Lamalem, Y., Cherradi, B., Saleh, S. & Raihani, A., Enhancing Skin Diseases Classification Through Dual Ensemble Learning and Pre-trained CNNs, International Journal of Advanced Computer Science and Applications, 14(6), pp.436-445, 2023.

Jahanifar, M., Tajeddin, N.Z., Koohbanani, N.A., Gooya, A. & Rajpoot, N., Segmentation of Skin Lesions and Their Attributes using Multi-scale Convolutional Neural Networks and Domain Specific Augmentations, arXiv preprint, 2018.

Bakkouri, I. & Afdel, K., Computer-Aided Diagnosis (CAD) System based on Multi-Layer Feature Fusion Network for Skin Lesion Recognition in Dermoscopy Images, Multimedia Tools and Applications, 79(29-30), pp.20483-20518, 2020.

Abunadi, I. & Senan, E.M., Deep Learning and Machine Learning Techniques of Diagnosis Dermoscopy Images for Early Detection of Skin Diseases, Electronics, 10(24), 3158., Dec. 2021.

Shaheen, H. & Singh, M.P., Multiclass Skin Cancer Classification using Particle Swarm Optimization and Convolutional Neural Network with Information Security, Journal of Electronic Imaging, 32(4), pp.042102-042102, 2023.

Raghavendra, P.V., Charitha, C., Begum, K.G. & Prasath, V.B.S., Deep Learning?based Skin Lesion Multi-class Classification with Global Average Pooling Improvement, Journal of Digital Imaging, 36(5), pp.2227-2248, 2023.

Thanka, M.R., Edwin, E.B., Ebenezer, V., Sagayam, K.M., Reddy, B.J., Gerhan, H. & Emadifar, H., A Hybrid Approach for Melanoma Classification using Ensemble Machine Learning Techniques with Deep Transfer Learning, Computer Methods and Programs in Biomedicine, 3, 100103, 2023.

Alani, A.A., Altameemi, H.G., Azeez Asmael, A.A. & Al-Obaidi, M.A., An Effective Multiclass Human Skin Lesion Diagnosis System based on Convolutional Neural Networks, Automatic Control and Computer Sciences, 57(2), pp.135-142, May, 2023.

Adegun, A.A. & Viriri, S., FCN-based Densenet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images, IEEE Access, 8, pp.150377-150396, 2020.

Mehmood, A., Gulzar, Y., Ilyas, Q.M., Jabbari, A., Ahmad, M. & Iqbal, S., SBXception: A Shallower and Broader Xception Architecture for Efficient Classification of Skin Lesions, Cancers, 15(14), 3604., July, 2023.

Goyal, M., Yap, M.H. & Hassanpour, S., Multi-class Semantic Segmentation of Skin Lesions via Fully Convolutional Networks, arXiv preprint, 2017.

Balaji, V.R., Suganthi, S.T., Rajadevi, R., Kumar, V.K., Balaji, B.S. & Pandiyan, S., Skin Disease Detection and Segmentation using Dynamic Graph Cut Algorithm und Classification Through Naive Bayes Classifier, Measurement, 163, 107922, 2020.

Al-Masni, M.A., Kim, D.H. & Kim, T.S., Multiple Skin Lesions Diagnostics via Integrated Deep Convolutional Networks for Segmentation and Classification, Computer methods and programs in biomedicine, 190, 105351, July, 2020.

Sonawane, R.S., Skin-cancer Classification using Deep Learning with DenseNet and VGG with Streamlit-framework Implementation, PhD dissertation, National College of Ireland, Dublin, 2023.

Usama, M., Naeem, M.A. & Mirza, F., Multi-class Skin Lesions Classification using Deep Features, Sensors, 22(21), 8311, 2022.

Gairola, A.K., Kumar, V., Sahoo, A.K., Diwakar, M., Singh, P. & Garg, D., Multi-feature Fusion Deep Network for Skin Disease Diagnosis, Multimedia Tools and Applications, 1-26, 2024.

Purni, J.T. & Vedhapriyavadhana, R., EOSA-Net: A Deep Learning Framework for Enhanced Multi-class Skin Cancer Classification using Optimized Convolutional Neural Networks. Journal of King Saud University-Computer and Information Sciences, 36(3), 102007, 2024.

Skin Diseases Image Dataset, (2021, August 16). Kaggle. https://www.kaggle.com/datasets/ismailpromus/skin-diseases-image-dataset

Tschandl, P., The HAM10000 Dataset, a Large Collection of Multi-Source Dermatoscopic Images of Common Pigmented Skin Lesions [Dataset], In Harvard Dataverse, 2018. https://doi.org/10.7910/dvn/dbw86t

Center for Artificial Intelligence in Medicine & Imaging, Shared datasets. https://aimi.stanford.edu/shared-datasets (11 September 2024)

Niloy, G.M., Bitto, A.K., Biplob, K.B.M.B., Sammak, M.H., Das, A. & Hridoy, G.G., MobileNet-Eye: An Efficient Transfer Learning for Eye Disease Classification. In 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS) (pp. 01-05), IEEE, March, 2024.

Bitto, A.K., Bijoy, M.H.I., Yesmin, S., Mahmud, I., Mia, M.J., Biplob, B. & Md, K.B., Tumor-Net: Convolutional Neural Network Modeling for Classifying Brain Tumors from MRI Images, International Journal of Advances in Intelligent Informatics, 9(2), pp.148-160, 2023.

Bitto, A.K., Bijoy, M.H.I., Yesmin, S. & Mia, M.J., Tumor-TL: A Transfer Learning Approach for Classifying Brain Tumors from MRI Images, In International Conference on Machine Intelligence and Emerging Technologies, pp. 177-186, 2023.