Exploring the Effectiveness of Optimized Convolutional Neural Network in Transfer Learning for Image Classification: A Practical Approach

Abstract

Listen

Translate article

Transfer learning is a popular deep learning technique that involves fine-tuning a pre-trained CNN model on a new dataset to improve accuracy and speed. This article examines the effectiveness of transfer learning techniques in image classification tasks using CNNs. The paper reviews recent studies on transfer learning techniques, including their use in medical image analysis applications such as COVID-19 detection and Alzheimer’s disease classification. The study discusses the ImageNet dataset as a benchmark for pretraining CNN models and proposes an optimized CNN model that uses various optimization techniques to improve performance and efficiency. The article also includes a comparison table of various image classification techniques, including CNN, RNN, SVM, RF, and optimized CNN, with the optimized CNN offering the best performance and computational efficiency. The study emphasizes the importance of selecting the appropriate technique for specific applications based on factors such as available hardware and desired tradeoff between training time and prediction time. Overall, transfer learning techniques are shown to be effective in image classification tasks, particularly when labeled data is limited.