Development Of Detection Model For Skin Diseases In Pets Using Image Processing And Deep Learning Techniques

Abstract

Early detection of skin diseases in pets is essential but often hindered by the cost and complexity of clinical diagnosis. This study introduces a deep learning–based system for identifying three common pet skin diseases—Ringworm, Scabies, and Earmite—using images captured with mobile phone cameras. The system integrates classical image preprocessing techniques, including Contrast Limited Adaptive Histogram Equalization (CLAHE) and Hue-Saturation-Value (HSV) segmentation, with a custom convolutional neural network (CNN) designed for disease-specific classification tasks.

Two separate models were developed: a multi-class CNN model for classifying Ringworm, Scabies, and Undetected conditions, which achieved a test accuracy of 83%, and a binary CNN model for classifying Earmite versus Undetected, which achieved 100% accuracy, precision, and recall on both test and unseen validation sets. Compared to transfer learning models such as ResNet-50 and VGG16, the proposed CNN models demonstrated superior performance under limited-data conditions (72 images total), emphasizing the advantage of domain-specific model design and preprocessing.

These findings suggest that disease-adapted CNN architectures, combined with targeted preprocessing, can support accurate and accessible veterinary screening using mobile devices. Future work will focus on expanding the dataset and deploying the model in a real-time mobile diagnostic application.