Canine Demodicosis Detection in Dogs using Deep Learning

Detecting canine diseases holds immense significance for timely and precise diagnosis, facilitating effective treatment and care. In this proposed system, canine disease detection is achieved using deep learning techniques such as CNN, focusing on normal images of dogs. The utilization of these images provides valuable insights into the dog’s condition and enables the identification of various diseases, including mange. Deep learning techniques are employed to automatically extract features from these normal images and classify them into different disease categories. Transfer learning is applied to leverage pre-trained models and enhance the performance of the disease detection system. The proposed methodology shows promising results with an accuracy of 97% in accurately detecting and classifying canine diseases, providing a valuable tool in diagnosing and treating dogs more effectively.