Enhancing Image Classification Accuracy with Adaptive Learning Convolutional Neural Network (AL-CNN): A Hybrid of AlexNet and LeNet Architectures

Image classification is a key application of computer vision that finds direct relevance in medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of AL-CNN (Adaptive Learning Convolutional Neural Networks) for image classification with results reported on a large-scale benchmark dataset which have been widely accepted for benchmarking test performance. The AL-CNN architecture integrates the use of both convolutional, pooling, and fully connected layers. This model was systematically trained on a subset of the dataset and then tested on an independent validation subset to test its efficiency and generalization capability. In addition, optimization techniques, including data augmentation, dropout, and advanced activation functions, were used to further enhance model performance. Results in terms of accuracy metrics indicated the successful execution of the AL-CNN model towards reliable and accurate image classification. This paper suggests the utility of AL-CNN technique to address various complexities of the problem of image classification, making room for innovations to take place in this domain.