Analysis And Diagnosis Using Deep -Learning Algorithm On Erythemato-Squamous Disease

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2021 by IJETT Journal
Volume-69 Issue-3
Year of Publication : 2021
Authors : Gopalakrishnan.S, Dr.Ebenezer Abishek.B, Dr. A. Vijayalakshmi, Dr. V. Rajendran
DOI :  10.14445/22315381/IJETT-V69I3P210

Citation 

MLA Style: Gopalakrishnan.S, Dr.Ebenezer Abishek.B, Dr. A. Vijayalakshmi, Dr. V. Rajendran  "Analysis And Diagnosis Using Deep -Learning Algorithm On Erythemato-Squamous Disease" International Journal of Engineering Trends and Technology 69.3(2021):52-57. 

APA Style:Gopalakrishnan.S, Dr.Ebenezer Abishek.B, Dr. A. Vijayalakshmi, Dr. V. Rajendran. Analysis And Diagnosis Using Deep -Learning Algorithm On Erythemato-Squamous Disease  International Journal of Engineering Trends and Technology, 69(3),52-57.

Abstract
Dermatological diseases (Skin disease)are a common health issue that is caused because of various factors in the current period. A serious problem in dermatology is considered as a diagnosis of Erythemato-squamous disease (ESD), which is identified as one of the skin disease categories. This affects the skin by causing redness in the skin layer and also leads to loss and damage of the skin. This sort of dermatological issue occurs because of environmental and genetic factors. Here we develop different machine-learning techniques, which can diagnose Erythemato-squamous disease. To help the experts in the field of medicine for the purpose of disease diagnosis, the classification and acknowledgment frameworks have been improved in a higher aspect. Here to diagnose the ESD, we have developed distinct techniques in machine-learning. Certain illnesses, for example, lichen planus, seboreic dermatitis, pityriasis rubra pilaris, pityriasis rosea, persistent dermatitis&psoriasisare the six-skin class condition which is arranged under ESD. The diagnosis of automatic on ESD couldaid dermatologists& specialists in diminishing endeavors on their side and accepting immediate decisions on treatment. This writing is loaded with an activity that utilized customary AI strategies for the finding of ESD. Be that as it may, there aren`t numerous occurrences of the use of Deep-learning for the analysis of ESD.

Reference
[1] Zhang X, Zhou B, Osborn T, Bartholmai B, Kalra S. Lung Ultrasound Surface Wave Elastography for Assessing Interstitial Lung Disease. IEEE Trans Biomed Eng. 2019 May;66(5)(2018) 1346-1352. doi: 10.1109/TBME.2018.2872907. Epub 2018 Oct 1. PMID: 30281430; PMCID: PMC6541007.
[2] Gu Y, Ge Z, Bonnington CP, Zhou J. Progressive Transfer Learning and Adversarial Domain Adaptation for Cross-Domain Skin Disease Classification. IEEE J Biomed Health Inform. 24(5)(2020) 1379-1393. doi: 10.1109/JBHI.2019.2942429. Epub 2019 Sep 23. PMID: 31545748.
[3] A. A. Adegun and S. Viriri, FCN-Based DenseNet Framework for Automated Detection and Classification of Skin Lesions in Dermoscopy Images, in IEEE Access, 150377-150396, 8(2020), doi: 10.1109/ACCESS.2020.3016651.
[4] Verma, A.K., Pal, S. Prediction of Skin Disease with Three Different Feature Selection Techniques Using Stacking Ensemble Method. ApplBiochemBiotechnol 191(2020) 637–656. https://doi.org/10.1007/s12010-019-03222-8
[5] S. Nitkunanantharajah, G. Zahnd, M. Olivo, N. Navab, P. Mohajerani, and V. Ntziachristos, Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming, in IEEE Transactions on Medical Imaging, 39(2)(2020) 458-467, doi: 10.1109/TMI.2019.2928393.
[6] Galvez JM, Castillo-Secilla D, Herrera LJ, Valenzuela O, Caba O, Prados JC, Ortuno FM, Rojas I. Towards Improving Skin Cancer Diagnosis by Integrating Microarray and RNA-Seq Datasets. IEEE J Biomed Health Inform. 24(7)(2020) 2119-2130. doi: 10.1109/JBHI.2019.2953978. Epub 2019 Dec 23. PMID: 31871000.
[7] S. Putatunda, Care2vec: A deep learning approach for the classification of self-care problems in physically disabled children, arXiv:1812.00715 [cs.LG], (2018).
[8] A. M. Elsayad, M. Al-Dhaifallah, A. M. Nassef, Analysis and Diagnosis of Erythemato-Squamous Diseases Using CHAID Decision Trees, in 15th International Multi-Conference on Systems, Signals, and Devices (SSD), IEEE, 2018. doi:10.1109/SSD.2018.8570553.
[9] M. E. B. Menai, Random forests for automatic differential diagnosis of erythemato–squamous diseases, International Journal of Medical Engineering and Informatics 7(2015).
[10] Y. Luo, A. R. Sohani, E. P. Hochberg, P. Szolovits, Automatic lymphoma classification with sentence subgraph mining from pathology reports, Journal of the American Medical Informatics Association 21(2014) 824–832.
[11] J. Cheng, J. Liu, Y. Xu, F. Yin, D. W. K. Wong, N.-M. Tan, D. Tao, C.-Y. Cheng, T. Aung, T. Y. Wong, Superpixel classification based optic disc and optic cup segmentation for glaucoma screening, IEEE Transactions on Medical Imaging 32(2013) 1019–1032.
[12] N. Badrinath, G. Gopinath, K. Ravichandran, Design of automatic detection of erythema to-squamous diseases through a threshold-based abc-felm algorithm, Journal of Artificial Intelligence 6(2013) 245–256.
[13] L. Tang, M. Niemeijer, J. M. Reinhardt, M. K. Garvin, M. D. Abramoff, Splat feature classification with application to retinal hemorrhage detection in fundus images, IEEE Transactions on Medical Imaging 32(2013) 364–375.
[14] J. Xie, C. Wang, Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato- squamous diseases, Expert Systems with Applications 38(2011) 5809–5815.
[15] Shakti Chourasiya, Suvrat Jain, A Study Review On Supervised Machine Learning Algorithms, IJETT International Journal of Computer Science and Engineering 6(8) (2019) 16-20.
[16] S. Lekkas, L. Mikhailov, Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases, Artificial Intelligence in Medicine 50(2010) 117–126.
[17] E. D. Ubeyli, E. Dogdu, Automatic detection of erythemato-squamous diseases using k-means clustering, Journal of Medical Systems 34(2010) 179–184.

Keywords
diagnose, machine learning, deep learning, Erythemato, squamous, disease.