2023
doi: 10.12720/jait.14.6.1273-1279
Improved Model for Skin Illnesses Classification Utilizing Gray-Level Co-occurrence Matrix and Convolution Neural Network
Zahraa Jabbar Hussein1,*
,
Ahmed Mohammed Hussein1 ,
Ghadeer Ibrahim Maki2 ,
Hadeel Qasem Gheni1
- 1Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
- 2Nursing Techniques Department, Technical Institute of Al-Diwaniyah, Al-Furat Al-Awsat Technical University, Najaf, Iraq
- Manuscript receivedMarch 12, 2023
- revisedJune 25, 2023
- acceptedJuly 19, 2023
- publishedNovember 27, 2023
Abstract
Skin conditions are more common than other illnesses. Skin issues can be brought on by viruses, bacteria, allergies, fungi, etc. The detection of skin diseases has been made better by lasers and photonics since it is now quicker and more precise. However, such a diagnosis is pricey. A system for automated dermatology screening is built with the use of computer vision. Using the Gray Level Co-occurrence Matrix (GLCM) and Convolution Neural Network (CNN) provides an improved model for accurately diagnosing skin disorders. In order to classify skin photos, CNN is used by the model to extract features from the images using GLCM. The high-level features utilizing the statistical features were retrieved via GLCM separately because the photos utilized in the research are for skin conditions. Once merged, these features created a high-accuracy categorization. Two distinct classification processes are used to categorize photos into 13 diseases: First, the Deep Neural Network (DNN) classifier obtains 96.69% accuracy, 96.2% recall, 96.2% precision, and 96.2% F1-score in terms of performance evaluation measures. Second, accuracy, recall, precision, and F1-score are the performance evaluation metrics for the Multiple Support Vector Machine (MSVM) classifiers. The model outperforms other cutting-edge models in terms of accuracy and effectiveness when compared to them. This work thus indicates the capability of GLCM and CNN for the classification of skin diseases and their prospective uses in the healthcare sector.
Keywords
- deep learning
- convolution neural network
- gray-level co-occurrence matrix
- skin disease
How to Cite
Zahraa Jabbar Hussein, Ahmed Mohammed Hussein, Ghadeer Ibrahim Maki, and Hadeel Qasem Gheni, "Improved Model for Skin Illnesses Classification Utilizing Gray-Level Co-occurrence Matrix and Convolution Neural Network," Journal of Advances in Information Technology, vol. 14, no. 6, pp. 1273-1279, 2023. https://doi.org/10.12720/jait.14.6.1273-1279
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
