CNN Architectures for Robust Image Classification

Image classification is a foundational task in computer vision with wide-ranging applications in healthcare diagnostics and autonomous systems. In this work, we design and implement a Convolutional Neural Network (CNN) model for robust image classification using TensorFlow and Keras. Key preprocessing techniques such as normalization and data augmentation are employed to enhance model generalization. The CNN architecture is fine-tuned through hyperparameter tuning, including adjustments to convolutional, pooling, and dense layers. Performance is evaluated using accuracy, precision, recall, and F1-score metrics. Experimental results demonstrate superior classification performance and effective pattern recognition across image classes when compared to other architectures. This study supports the potential of deep CNN architectures for automated image analysis and highlights avenues for future improvements through deeper networks and larger datasets.