IMPROVE GOOGLENET ARCHITECTURE TO ENHANCE IMAGE CLASSIFICATION ACCURACY

GoogLeNet (Inception v1) introduced a computationally efficient deep convolutional neural network architecture that achieved state-of-the-art performance in large-scale image classification, notably in the ImageNet Large Scale Visual Recognition Challenge. Its Inception module enabled multi-scale feature extraction while significantly reducing the number of parameters compared to earlier networks. However, advances in deep learning-such as residual learning, attention mechanisms, improved normalization, and optimized training strategies-have surpassed the original design in both accuracy and robustness. This paper proposes architectural enhancements to GoogLeNet aimed at improving image classification accuracy without sacrificing computational efficiency. The proposed improvements include integrating residual connections into Inception modules, incorporating channel and spatial attention mechanisms, refining convolution factorization, applying batch normalization systematically, and adopting modern optimization strategies. Through conceptual analysis and practical examples across datasets such as ImageNet and CIFAR-10, the enhanced architecture demonstrates improved convergence stability, stronger feature representation, and higher classification accuracy. The findings indicate that modernizing GoogLeNet with contemporary deep learning innovations significantly enhances its competitiveness for current computer vision applications.