2020· International Journal of Applied Data Science & Modern Computing· 0 citations
TL;DR
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.
Abstract
Image classification is a major area in computer vision, driven by rapid advances in deep learning. Over the last decade, convolutional neural networks (CNNs) and their variants have achieved high performance in applications such as medical diagnosis, autonomous driving, industrial inspection, remote sensing, and biometrics. However, choosing the right model remains challenging due to trade-offs between accuracy, computational cost, efficiency, and robustness. This paper presents a comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures. Using a common evaluation framework and standard datasets, the study analyzes performance based on key design factors such as depth, width, receptive field, skip connections, and normalization. Theoretical concepts like convolution operations, residual learning, and optimization are also discussed.The results show that deeper networks provide better representation, while residual connections and compound scaling improve training stability and efficiency. Lightweight models perform well in resource-limited and real-time environments. Overall, the study offers practical guidance for selecting suitable architectures and highlights future research areas such as neural architecture search, self-supervised learning, and efficient model deployment.
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
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
Deep learning has evolved into a major computational paradigm for solving complex problems involving image understanding, remote sensing, human activity recognition, graph-based learning, and other data-intensive applications. However, the increasing diversity of deep learning architectures has made model selection and performance assessment more difficult because predictive effectiveness must be evaluated alongside computational complexity, robustness, scalability, and deployment requirements. This article presents a systematic analysis of deep learning models by synthesizing existing research on fundamental deep learning techniques, neural network architectures, application-specific models, robustness, and hardware acceleration. The review comparatively examines conventional deep learning approaches, convolutional neural networks, graph convolutional neural networks, robust deep learning models, and application-oriented architectures. Particular attention is given to the relationship between model architecture and performance dimensions such as accuracy, generalization, computational efficiency, scalability, and practical deployability. The analysis indicates that no single deep learning architecture is universally optimal; rather, performance depends strongly on data characteristics, task requirements, architectural complexity, and computational resources. CNN-oriented models demonstrate strong suitability for spatial and visual information, graph-based approaches provide advantages for relational data but introduce substantial acceleration challenges, and robust learning approaches address reliability concerns that conventional accuracy-oriented evaluation may overlook. The study establishes a multidimensional framework for assessing deep learning performance and identifies the need for evaluation strategies that jointly consider predictive capability, robustness, computational cost, and deployment constraints.
D. Iliev, Dr. Elena Stojanovsk· International Journal of Nex...· 0 citations
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.
Baffoe Nicholas, Jephthah Kwame Lanor· Andalasian International Jou...· 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.
Image classification is a well-known problem in image processing, computer vision, and machine learning. We investigate picture categorization using deep learning in this research. Nowadays bird watching is becoming a common hobby for everyone, but more than 10,000 species are part of the ecosystem, which causes difficulty in identification and prediction. Additionally, the birds may appear in different scenarios and also in different shapes, sizes, and colors. So, we use Convolutional Neural Network (CNN) to build the models, decreasing the dimensionality of images without losing any content by using a built-in convolutional layer. This will identify the input given by the user and starts the image processing and then compares it with a trained model and predicts the species of the bird. The model will return the output with the predicted probability of the species. If the user-given image is not available in the dataset, the model automatically adds it to the dataset which will be useful in building the dataset. This model helps in the classification and recognition of the birds. Key words: Deep Learning (DL) , Convolution Neural Network(CNN) ,Image Classification.
Keywords—component, formatting, style, styling, insert
D. M. G. Prasuna, M. U. Rani, Ram Mohan Pamu· International Journal of Cre...· 0 citations
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