This article delves deep into the advancements in image classification using neural networks, exploring how neural networks have achieved success in solving
In this paper, we have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to
In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from. LeNet-5 to
In this paper, We have explained different CNN architectures for image classification. Through this paper, we have shown advancements in CNN from LeNet-5 to
I. INTRODUCTION Moreover, this review emphasizes the evolutionary. trajectory of CNN architectures, showcasing the. In the past few years, Convolutional Neural
Keywords—Convolutional Neural Networks (CNNs); AlexNet; image classification; transfer learning; MNIST Dataset; Custom CNN Architecture; deep learning; model training and evaluation; neural network optimization; activation functions; feature extraction; machine learning; pattern recognition; data preprocessing; loss functions; model accuracy I. B. Research Questions 1) How do different Convolutional Neural Network (CNN) architectures, such as AlexNet and custom-designed models, perform in terms of accuracy and efficiency when applied to various image classification tasks? C. Expected Benefits 1) Improved Accuracy is Enhanced classification accuracy due to the advanced capabilities of deep learning models in capturing complex image features. This approach stabilizes and accelerates the training of deep networks by reducing internal covariate shift, allowing for higher learning rates and improved convergence, thereby enhancing model performance in image recognition tasks [6]. The model was trained using a Convolutional Neural Network (CNN) architecture, enhanced with residual connections, batch normalization, and dropout layers to improve performance and generalization.
Convolutional Neural Networks (CNNs) have gradually replaced traditional methods in image classification due to the rise of deep learning, resulting in improved
This period has seen a shift from traditional convolutional neural networks (CNNs) to more sophisticated models, including Vision Transformers,