Journals / Journal of Investigations on Engineering and Technology / 2022 / Cilt: 5 - Sayı: 1

Investigation of the eczema and skin cancer disease diagnosis by using image processing techniques

Pages
47–62
Publication Date
2022-06-30
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

According to the results obtained, the accuracy, sensitivity, and selectivity ratios of decision trees are obtained compared to image processing.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:It is seen that many diseases, especially dermatological diseases, arise due to bad weather conditions such as high temperature, dust, smoke, and sun in the environment.
📊
Key Findings & Data:The most common diseases are eczema caused by malnutrition, soil, bacteria, bad food, and other factors, and risky moles, which are usually caused by excessive sunlight or during childbirth.
💡
Conclusion & Impact:According to the results obtained, the accuracy, sensitivity, and selectivity ratios of decision trees are obtained compared to image processing.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:eczama · MATLAB · image processing

Abstract

It is seen that many diseases, especially dermatological diseases, arise due to bad weather conditions such as high temperature, dust, smoke, and sun in the environment. The most common diseases are eczema caused by malnutrition, soil, bacteria, bad food, and other factors, and risky moles, which are usually caused by excessive sunlight or during childbirth. Due to all these environmental, physiological, and chemical factors, it is important to quickly detect all existing skin diseases, especially eczema and risky moles, and it has become inevitable to establish a less costly diagnostic system with the help of doctors to prevent the aggravation of the diseases. If eczema and risky skin problems progress, they will be difficult to treat and take a long time. For this reason, the research aims to take an image from the infection site and then process this image in many ways in a MATLAB environment to obtain an output that can help doctors in their work. Differently, in this study, diseases were classified by the decision tree method using the clinical data of the related image. In addition, it is seen that it is determined in normal depth together with the idea developed originally. Decision trees supported the currently used image processing and classification method, and the results of both methods are also compared with this method. According to the results obtained, the accuracy, sensitivity, and selectivity ratios of decision trees are obtained compared to image processing. The software used gives a warning when the image processing and decision tree methods give conflicting results. If this occurs, it is necessary to stick to the doctor's data. The system in this study aims to improve human life and make it safe by recognizing eczema and risky moles. In this study, samples were selected from various layers of the body. In addition, a different interpretation can be made in the normal situation. When this approach technique is applied, more appropriate results have emerged in the process of detecting eczema and risky moles. In addition, normal skin is also involved in the process. Being able to define the normal state has been a contribution to science and it is aimed in this study to facilitate the work of medical personnel.

🔖

Cite This Article

ERDEM, Y. S., ÖZKAN, Ö. (2022). Investigation of the eczema and skin cancer disease diagnosis by using image processing techniques. Journal of Investigations on Engineering and Technology, Cilt: 5 - Sayı: 1, 47–62. https://ajindex.com/articles/777474

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (15)

  1. Kaplan, M. Implementation of an Auxiliary System for the Diagnosis of Skin Diseases Using Artificial Neural Networks, TC Fırat University, Institute of Science, Master Thesis, 2016
  2. Season, V. (2010). Epidemiology of dermatological diseases, disease burden, and place in primary care. Turkey Clinics J. Fam Md-Special Topics, 2010; 1(2): 15-20
  3. Alonso, DH., Wernick, MN., Yang, Y., Germano, G., Berman, DS., Slmoka, P. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol. https://doi.org/10.1007/s12350-017-0924-x, 2018
  4. Narula, S., Shameer, K., Salem Omar, AM., Dudley, JT., Sengupta, PP. Reply Deep learning with unsupervised features in echocardiographic imaging. J Am Coll Cardiol; 2017, 69: 2101–2
  5. Esteva, A., Kupre, B., Novoa, RA., et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature, 2017, 542:115–8
  6. Cichosz, SL., Johansen, MD., Hejlesen, O. Toward big data analytics: a review of predictive models in the management of es and its complications. J es Sci Technol, 2015, 10(1):27-34
  7. Tran, BX,. Latkin, CA., Giang, VT., et al., The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis. Int. J. Environ. Res. Public Health, 2019, 16:2699
  8. Char, DS., Shah, NH., Magnus, D. Implementing Machine Learning in Health Care, Addressing Ethical Challenges. N. Engl. J. Med., 2018, 378: 981–983
  9. Celebi, V., Inal, A. Problem of Ethics in the Context of Artificial Intelligence. The Journal of International Social Research, 2019, 12, 66
  10. Mujumdar, A., Vaidehi, V. Dibetes Prediction Using Machine Learning Algorithms. Procedia Computer Science, 2019, 165: 292–299
  11. Farid,D., Sadeghi,H., Hajigol,E. ve Parirooy,N. Classification of Bank Customers by Data Mining: a Case Study of Mellat Bank branches in Shiraz, International Journal of Management Accounting and Economics, 2016, 3: 534-543
  12. Walsh, S. Applying Data Mining Techniques Using SAS® Enterprise Miner- Course Notes, SAS Institute Inc., North Carolina, 2005
  13. Pratt, W. K., Digital Image Processing. USA: John Wiley & Sons, 2007
  14. Nixon, M. S., Aguado, A. S., Feature Extraction, and Image Processing. Newnes, UK, 2002
  15. Kaur, H., Kumari, V. Predictive modeling, and analy