AN INTELLIGENT SYSTEM FOR DOG BREED RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS - KRASNOSHLYK, Nataliya; BYLYMENKO , Anna (Cherkasy University Bulletin: Applied Mathematics. Informatics, 2023)
Introduction. Convolutional neural networks (CNNs) have established themselves as a powerful tool for image analysis. Inspired by biological visual processing, they automatically detect complex patterns in data by sequentially extracting increasingly abstract features. The growing demand for AI-powered interactive applications makes automated image recognition systems particularly relevant across a wide range of domains, including veterinary practice, animal shelters, and mobile applications. The Purpose of the article is to theoretically justify and practically demonstrate the use of convolutional neural networks for dog breed identification, and to describe the full development cycle of a CNN-based web application – from dataset preparation to deployment. Results. The article presents a review of image classification methods based on deep learning, with a focus on the architecture and training principles of CNNs. The roles of convolutional layers, pooling layers, the ReLU activation function, and the backpropagation algorithm are discussed. Three Python libraries central to the project are reviewed: Pandas for data preprocessing, TensorFlow for building and training the neural network model, and Streamlit for rapid development and deployment of the web interface. The practical implementation covers all stages of system development. A dataset of 1,888 images across 21 dog breeds was collected using the bing-image-downloader library and manually filtered. The images were split into training (90%) and validation (10%) sets. The CNN architecture follows a sequential structure consisting of four convolutional-pooling blocks with 16, 32, 64, and 128 filters respectively, followed by two fully connected layers (1,024 and 256 neurons) with Dropout regularization (p = 0.2), and a Softmax output layer with 21 neurons. The model was trained for 30 epochs and achieved a classification accuracy of 93.64% on the test set. Practical testing confirmed high accuracy for breeds present in the training data (e.g., Husky – 94.08%, Jack Russell Terrier – 99.98%) and predictable probabilistic distribution for breeds outside the dataset. The web application was deployed via Streamlit Cloud integrated with a GitHub repository, providing a user-friendly interface for uploading images and displaying the top five predicted breeds with corresponding probabilities. Conclusion. Convolutional neural networks are an effective tool for dog breed classification tasks. The combination of TensorFlow for model development and Streamlit for interface deployment provides an efficient and accessible pipeline for building machine learning–based web applications. The proposed approach can be readily extended to other image recognition tasks and illustrates the practical potential of deep learning in real-world automated recognition systems.