Jul 2026· Andalasian International Journal of Applied Science Engineering and Technology· 0 citations· 19 references
Abstract
This study presents a comparative analysis of four deep learning architectures: Simple Multi-Layer Perceptron (MLP), Deep MLP, Basic Convolutional Neural Network (CNN), and LeNet Convolutional Neural Network (CNN). The models are evaluated on the MNIST handwritten digit dataset under identical experimental conditions using CPU-only hardware. Each model is assessed based on classification accuracy, training time, and number of trainable parameters. Experimental results demonstrate that convolutional architectures consistently outperform fully connected architectures in classification accuracy, with the Basic CNN achieving the highest validation accuracy of 99.15% and the LeNet CNN offering the best balance between performance and efficiency with only 61,706 trainable parameters. The findings confirm that architectural design has a greater influence on model performance than the number of layers or parameters alone. This study provides a comprehensive review of the relevant literature, detailed architectural descriptions, experimental methodology, and a thorough analysis of results to support the comparative conclusions.
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency.
Riyaz Mohammed· International Journal of App...· 0 citations
In this paper, we perform a systematic empirical study of deep learning techniques on the CIFAR-10 image classification benchmark. We study three inter-related aspects of neural network design: (1) the relative impact of nine gradient descent optimisation algorithms on a baseline Multi-Layer Perceptron (MLP); (2) the effectiveness of six regularisation techniques namely; L2 weight decay, Dropout, Data Augmentation, Gaussian Noise Injection, Early Stopping, and Ensemble Learning; on model generalisation; and (3) a benchmark comparison of five landmark Convolutional Neural Network (CNN) architectures alongside Inception V3 trained from scratch. We also investigate autoencoder-based image reconstruction and a side-by-side comparison of linear Principal Component Analysis (PCA) with non-linear autoencoder representations. Nesterov Accelerated Gradient achieves the lowest training loss (1.5814) in short-run experiments. VGGNet achieves the highest test accuracy (75.09%) among CNN architectures trained from scratch for five epochs. Early Stopping yields the best regularisation outcome, and non-linear autoencoders outperform PCA in reconstruction quality. All experiments use TensorFlow 2.x and Keras on CIFAR-10.
Akber Hussain, M. Sajid, Abdul Raheem et al.· 2026 4th International Confe...· 0 citations
This study compares a custom CNN with three pre-trained models on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories, and shows that pre-trained models performed much better than the customised model.
Oluwadamilare (Asabia) Joseph Omoniyi, Omotosho Olawale Jacob, Ajaegbu Chigozirim et al.· International journal of res...· 0 citations
The results indicate the successful implementation of the proposed AL-CNN model for reliable and accurate image classification, with results reported on a large-scale benchmark dataset that is widely accepted for performance evaluation.
M. Chawla, Rashmi Agrawal, Bharat Bhushan· Bulletin of Electrical Engin...· 0 citations
Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.
A lightweight CNN architecture that achieves competitive performance without relying on pretrained models or transfer learning approaches, making it suitable for deployment on resource-constrained devices and balanced classification performance across all classes is developed.
