Transfer Learning for Medical Image Classification Using CNN Architectures

Abstract

Medical image classification plays a vital role in disease diagnosis, where accurate and timely interpretation of imaging data is essential for effective treatment. This paper presents a Transfer Learning-based framework for Medical Image Classification using Convolutional Neural Network architectures. The proposed system leverages pre-trained deep learning models such as VGG, ResNet, and Inception, which are fine-tuned on domain-specific medical imaging datasets to improve classification performance. Transfer learning enables the reuse of learned features from large-scale datasets, reducing the need for extensive labeled medical data and accelerating training. Image preprocessing and data augmentation techniques are applied to enhance model generalization and robustness. The framework is evaluated using standard performance metrics, demonstrating high accuracy and reliability across multiple medical imaging tasks. Additionally, integration with prior research in machine learning and healthcare analytics enhances the robustness and adaptability of the system. The study highlights the effectiveness of transfer learning in developing scalable and efficient medical image classification systems, particularly in resource-constrained healthcare environments.