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Dog Breed Classification Is A Challenging Computer Vision Task Due To High Intra-class Variation, Inter-class Similarity, And The Large Number Of Breeds With Subtle Visual Differences. Traditional Methods Relying On Handcrafted Features And Shallow Classifiers Often Fail To Achieve Satisfactory Performance In Real-world Scenarios. Recent Advancements In Deep Learning, Particularly Convolutional Neural Networks (CNNs), Have Demonstrated Remarkable Success In Image Recognition Tasks By Automatically Learning Discriminative Features From Data. This Study Proposes A Breakthrough Conventional-based Approach For Dog Breed Classification That Integrates CNNs With Transfer Learning Techniques. Pre-trained Deep Learning Models Such As VGG16, ResNet, And Inception Are Fine-tuned On A Curated Dog Breed Dataset To Leverage Their Feature Extraction Capabilities While Reducing Computational Cost And Training Time. The Proposed Method Enhances Classification Accuracy, Robustness, And Generalization Compared To Training Models From Scratch. Experimental Results Highlight The Effectiveness Of Transfer Learning In Achieving Superior Performance Across Multiple Dog Breeds, Offering A Scalable And Efficient Solution For Applications In Veterinary Research, Animal Identification, And Intelligent Pet-care Systems.