The results demonstrate that transfer learning significantly improves convergence speed, generalization, and computational efficiency, making it a promising approach for AI applications across domains such as healthcare, NLP, and autonomous systems.
Abstract
Transfer learning has become a key technique for improving the accuracy of neural networks in low-resource, low-data environments. The quantitative comparative analysis of the pre-trained models includes ResNet50, VGG16, BERT, GPT, and the baseline CNN and LSTM models. They are compared across three different application areas: computer vision, natural language processing (NLP), and medical imaging. The five benchmark datasets used were ImageNet, CIFAR-10, SST-2, IMDB, and Chest X-Ray. All experiments used the same preprocessing pipeline and evaluation metrics (accuracy, F1 score, precision, recall, and ROC-AUC). Results showed that models trained on the pre-trained data achieved consistently greater accuracy than the baselines in all domains (9-20%) and F1-score (0.09-0.16) gains. ResNet50 achieved 92% accuracy on CIFAR-10, compared to 72% for the CNN baseline, whereas BERT hit 92% on SST-2, with 80% accuracy for LSTM. VGG16 improved the accuracy of Chest X-Ray classification from 78% to 87% and reduced training time by up to 60%. There were a few instances of minor overfitting and domain mismatch, emphasizing the need for adaptive fine-tuning strategies. The results demonstrate that transfer learning significantly improves convergence speed, generalization, and computational efficiency, making it a promising approach for AI applications across domains such as healthcare, NLP, and autonomous systems.
Convolutional Neural Networks (CNNs) have significantly changed image classification over the years by allowing computers to learn features directly from raw pixel data. However, deciding between building a customised model and using a pre-trained one can be a difficult task, especially when working with small datasets. In this study, we compare a custom CNN with three pre-trained models—VGG16, ResNet50, and MobileNetV2—on the CIFAR-10 dataset, which comprises 60,000 colour images (32×32 pixels) across 10 categories. We measured model performance using accuracy, precision, recall, F1-score, and training time. The results show that pre-trained models performed much better than the customised model. ResNet50 had the highest accuracy at 92.4%. However, MobileNetV2 gave the best mix of speed (1,800 seconds to train) and accuracy (90.2%). The custom CNN reached 82.3% accuracy, used less memory, and did not need image resizing. These results offer clear benchmarks for choosing models in the face of limited resources. They also demonstrate that transfer learning can achieve strong performance, while showing that custom CNNs remain useful for learning and simple tasks.
Oluwadamilare (Asabia) Joseph Omoniyi, Omotosho Olawale Jacob, Ajaegbu Chigozirim et al.· International journal of res...· 0 citations
This study systematically compares seven pre-trained feature extractors across three architectural families, convolutional neural networks (CNNs), Vision Transformers (ViTs), and self-supervised models to provide practical guidance on model selection for downstream deep learning tasks.
Rafeek Sibrikhan, M. Mufassirin· Sri Lankan Journal of Techno...· 0 citations
Experimental findings show that pretrained models outperform those trained from scratch in terms of accuracy, convergence speed, and robustness, and a unified framework is proposed to integrate both processes in a deep learning pipeline.
Kwame Nkosi· International Journal of App...· 0 citations
Hyperparameter optimization is a critical determinant of the performance and generalizability of convolutional neural networks (CNNs), yet selecting optimal configurations remains a complex challenge due to the large, non-linear search space. While numerous optimization techniques have been explored in the literature, most comparative studies have relied on benchmark datasets, limiting insights into their practical effectiveness under real-world constraints. This study evaluates four widely used hyperparameter optimization methods (i.e., Grid Search, Optuna, Hyperopt, and DEAP) across five CNN architectures (i.e., AlexNet, GoogLeNet, LeNet, MobileNet, and ResNet18) under two distinct data regimes: the Modified National Institute of Standards and Technology (MNIST) benchmark and a custom SkinCancer image dataset. On MNIST, all configurations achieved strong classification performance, with test accuracies ranging from 95.45% to 97.61%, led by ResNet18 optimized via Grid Search (97.61%). In this simpler setting, DEAP provided the shortest optimization runtimes, while Optuna and Hyperopt offered an effective balance between performance and computational cost. In contrast, the SkinCancer dataset exhibited substantially greater variability in performance, highlighting the stronger influence of both model architecture and optimization strategy. ResNet18 achieved the highest overall test accuracy (81.50%) when optimized with Hyperopt. Although no single optimization method consistently outperformed all others, adaptive approaches frequently achieved competitive or superior predictive performance while requiring considerably less computation than exhaustive search. These findings demonstrate that hyperparameter optimization dynamics depend heavily on dataset complexity, where computational efficiency is the primary differentiator for simpler classification tasks, but optimization architecture selection becomes critical for navigating challenging medical imaging applications.
Sarab Almuhaideb, Ahmad Raza Khan· Applied Sciences· 0 citations