Enhancing image classification accuracy with AL-CNN: a hybrid of AlexNet and LeNet architectures

Abstract

Image classification is a key application of computer vision with direct relevance to medical diagnostics, autonomous vehicles, and remote sensing. This paper discusses the use of an adaptive learning convolutional neural network (AL-CNN) for image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation. The AL-CNN architecture integrates convolutional, pooling, and fully connected layers. The model was systematically trained on a subset of the dataset and subsequently tested on an independent validation subset to evaluate its efficiency and generalization capability. In addition, optimization techniques such as data augmentation, dropout, and advanced activation functions were employed to further enhance model performance. The results, based on accuracy metrics, indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification. This study demonstrates the potential of the AL-CNN approach to address various complexities in image classification, thereby enabling further innovation in this domain.