Detection and classification of skin diseases with ensembles of deep learning networks in medical imaging

A. Kalaivani; S. Karpagavalli

doi:10.53730/ijhs.v6nS1.8402

Authors

A. Kalaivani Assistant Professor, Department of Computer Technology, Nallamuthu Gounder Mahalingam College, Pollachi & Research Scholar, Department of Computer Science, PSGR Krishnammal College for Women., Coimbatore, Tamilnadu, India
S. Karpagavalli Associate Professor, Department of Computer Science, PSGR Krishnammal College for WomenCoimbatore, Tamilnadu, India

Keywords:

Classification, Deep learning, Dermatology, Ensemble technique, Skin disorders

Abstract

Skin disorders are a serious worldwide public health issue that affects a large number of individuals. In recent years, with the fast advancement of technology and the use of different data mining approaches, treatment of skin predictive classification has really become highly predictive as well as accurate. As a result, the type of machine learning approaches capable of efficiently differentiating skin condition categorization is essential. So far, no one machine learning approach has outperformed the others in terms of skin disease prediction. In this research, we introduce a new method that combines two separate data mining approaches into a single unit, as well as an ensemble approach that combines both data mining techniques into a single group. We explore different data mining strategies to categorize the skin condition using an informative Dermatology publicly accessible dataset ISIC2019 images, and then apply an ensemble deep learning method. Furthermore, the presented ensemble technique, which is based on machine and deep learning, was tested on Dermatology datasets and was able to categorize skin disorders into seven categories.

Downloads

Download data is not yet available.

References

Belal, A. M.-M. (2017). Discriminative feature learning for skin disease classification using deep convolutional neural network. International Conference on Environment and Electrical Engineering, IEEE Explore, 589-695.

Codella, N. C. (2017). Deep learning ensembles for melanoma recognition in dermoscopy images. IBM Journal of Research and Development, 61(4), 1-6.

H., A. G., Anitha, J., & J., J. P. (23-24 March 2018). Identification of Melanoma in Dermoscopy Images Using Image Processing Algorithms. In Proceedings of the 2018 International Conference on Control, Power, Communication and Computing Technologies (ICCPCCT). doi:DOI:10.1109/ICCPCCT.2018.8574277

Kittipat Sriwong, S. B. (2019). Dermatological Classification Using Deep Learning of Skin Image and Patient Background Knowledge. International Journal of Machine Learning and Computing, 9(9), 862-867. doi:doi: 10.18178/ijmlc.2019.9.6.884

Kwetishe Danjuma, A. O. (2014). Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis. IJCSI International Journal of Computer Science, 11(6), 85-94.

Li-sheng Wei, Q. G. (2018). Skin Disease Recognition Method Based on Image Color and Texture Features. Computational and Mathematical Methods in Medicine, 2018, 1-10. doi:https://doi.org/10.1155/2018/8145713

Parvathaneni Naga Srinivasu, J. G. (2021). Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM. Sensors, 21. doi:https://doi.org/10.3390/s21082852

Robinson, J. (2005). Sun exposure, sun protection and vitamin D. The Journal of the American Medical Association, 294, 1541-1543.

S. M. , K., J. R. , K., & K. , G. (2019). Skin Cancer Diagnostic using Machine Learning Techniques-Shearlet Transform and Naive Bayes Classifier. Int. J. Eng. Adv. Technol., 9(2), 3478-3480.

Sumithra R, S. M. (2015). Segmentation and Classification of Skin Lesions for Disease Diagnosis. Proc Computer Science, 45-76. doi:https://doi.org/10.1016/j.procs.2015.03.090

Wu Z, Z. S. (2019). Studies on Different CNN Algorithms for Face Skin Disease Classification Based on Clinical Images. IEEE Access, 7, 66505–66511. doi:Digital Object Identifier 10.1109/ACCESS.2019.2918221

Y. C. , L., S.-H. , J., & H.-H. , W. (2018). WonDerM: Skin Lesion Classification with Fine-tuned Neural Networks. 178-196. doi:https://doi.org/10.48550/arXiv.1808.03426

Zeon Trevor Fernando, P. T. (2016). DOCAID: Predictive healthcare analytics using naive bayes classification. International Conference on Advances in Computing, 67-75.

Shrestha, A.; Mahmood, A.(2016). Improving Genetic Algorithm with Fine-Tuned Crossover and Scaled Architecture. J. Math. 401-584.

Saber, E.; Ruhul, S.; Daryl, E.(2013). A New Genetic Algorithm for Solving Optimization Problem. Eng. Appl. Artif. Intell. 27, 57–69.

Hsu, W.(2018). Bayesian Classification. Encyclopedia of Database Systems; Liu, L., Ozsu, M.T., Eds.; Springer: New York, NY, USA.

Tahmassebi, A.; Gandomi, A.; Schulte, M.; Goudriaan, A.; Foo, S.; Meyer-Base, A.(2018). Optimized Naive-Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification. Complexity 1–24.

Livieris, I.E.; Iliadis, L.; Pintelas, P. (2021). An ensemble techniques of weight-constrained neural networks. Evol. Syst. 12, 155–167.

Roy, K.; Chaudhuri, S.S.; Ghosh, S.; Dutta, S.K.; Chakraborty, P.; Sarkar, R.(2019). Skin Disease detection based on different Segmentation Techniques. In Proceedings of the 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India, 18–20 March 2019; pp. 1–5.

Zhang, X.; Wang, S.; Liu, J.; Tao, C.(2018). Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge. BMC Med. Inform. Decis. Mak.18, 59.

FA: Hersh F. Mahmood, Hooshang Dabbagh, Azad A. Mohammed, Comparative study on using chemical and natural admixtures (grape and mulberry extracts) for concrete, Case Studies in Construction Materials, Volume 15, 2021,

Kumar, S. (2022). A quest for sustainium (sustainability Premium): review of sustainable bonds. Academy of Accounting and Financial Studies Journal, Vol. 26, no.2, pp. 1-18

Allugunti, V.R. (2019). Diabetes Kaggle Dataset Adequacy Scrutiny using Factor Exploration and Correlation. International Journal of Recent Technology and Engineering, Volume-8, Issue-1S4, pp 1105-1110.

Yadav, S.S.; Jadhav, S.M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. J. Big Data 6, 1–18.