Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2020 by IJETT Journal
Volume-68 Issue-9
Year of Publication : 2020
Authors : Narendra Mohan
DOI :  10.14445/22315381/IJETT-V68I9P216


MLA Style: Narendra Mohan  "Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm" International Journal of Engineering Trends and Technology 68.9(2020):109-120. 

APA Style:Narendra Mohan. Recognition of Skin Diseases using Deep Neural Network Optimized by Group Teaching Algorithm  International Journal of Engineering Trends and Technology, 68(9),109-120.

The most commonly occurring diseases among all ages of people are the skin diseases. Every people have different skin conditions and these diseases create dangerous effects on the skin. It is essential to recognize the difference between the skin conditions to identify the diseases at its initial stage and to control them from spreading. This work aims to improve the accuracy of diagnostic systems using Image Processing and classification techniques. Basically, the classification of skin diseases undergoes five phase’s namely pre-processing, image segmentation, feature extraction, feature selection and classification. During pre-processing, the quality of original image gets enhanced and noise is removed. Image segmentation is done using FCM (Fuzzy C Means) combined with morphological operators. Then, the features from the segmented image are extracted using LBP (Local Binary Pattern)-GLCM (Gray Level Co-occurrence Matrix) extraction techniques. Sometimes the extracted features may found in large dimension with relevant and irrelevant features. In order to reduce this, an optimization based feature selection process named BGOA (Binary Grasshopper Optimization Algorithm) is involved before the classification phase. Finally, a deep learning based approach named DNN (Deep Neural Network) is used for classification which is optimized using GTO (Group Teaching Optimization) algorithm. The simulation analysis is carried out with PH2 dataset. The performance metrics like accuracy, specificity, and sensitivity are determined.


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Skin Disease, Deep Learning, Image Processing, GLCM, Group Teaching Optimization.