#Diagnosis of Canine Skin Diseases
• Developed a robust image processing and skin diagnosis system for dog skin diseases using advanced techniques like GLCM, HOG, and LBP. • Applied machine learning algorithms such as Support Vector Machines, Random Forest Classifier, Nearest Neighbors Classifier, XGBoost Classifier, and Voting Classifier to train and test the classification models. • Conducted extensive experimentation and evaluation to achieve high accuracy in dog skin disease classification. • Employed Boruta Feature Selection to identify and select the most relevant features, optimizing the classification models and improving efficiency. • Contributed to the field of veterinary medicine by developing an automated system for early detection and diagnosis of dog skin diseases, potentially improving the efficiency of treatment and reducing misdiagnosis