Human Skin Fungal Diseases Classification Using Deep Learning Technique


  • Tsedenya Debebe Information Technology, faculty of computing and Informatics, Jimma Institute of Technology /Jimma University, Jimma, Ethiopia
  • Berihun Molla Health Informatics, School of public Health, college of Medicine and Health Science /Arbaminch university , Arbaminch, Ethiopia



Skin Disease, Deep Learning, Image processing, MobileNetV2, ResNet 50, CNN


Skin plays a significant role in body temperature regulation. Several risks affect the skin, from the common cause of skin disorders are bacteria, viruses, and fungi. Identifying the disease based on manual feature extractions or the symptoms is time-Consuming and requires extensive knowledge for perfect identification. Previously research was done on Diagnosing, detecting, and classifying skin diseases. However, in the previous work, tinea species and tinea corporates are not identified, especially for black skin color. In this paper, we develop a CNN model to classify skin fungal disease types like tinea pedies, tinea capitis, tinea corporates, and tinea uniguium. Then softmax classifies images as tinea pedies, tinea capitis, tinea corporates, and tinea uniguium. We have collected 407 skin fungal lesion images from patients at Dr. Gerbi's medium clinic of Jimma and JUMC using the smartphone camera (Techno pop two power, Techno Spark4, SamsungA20). After collecting datasets, Image Preprocessing, Image augmentation techniques are applied to increase the performance of the human skin disease classification model. In this study, we have done image preprocessing (image size normalization, RGB to Grayscale conversion, and balancing the intensity of the image). We have normalized the images to three sizes which are 120 x120, 150X150, and 224x224. From the total augmented 1069 images, 80% (727) were for training, 10% (164) for validation, and the remaining 10% (178) for testing. After evaluating the model, we have registered an overall performance accuracy of 83% using our CNN-based HSFDC model. The accuracy achieved 79% and 69% for MobileNetV2 and ResNet 50, respectively. This implies that the developed model is better than the MobileNetV2 and ResNet50 pre-trained CNN Models for our dataset.




How to Cite

Debebe, T., & Molla, B. (2022). Human Skin Fungal Diseases Classification Using Deep Learning Technique. Harla Journal of Engineering and Technology, 1(2), 16–37.