Enhancing Diagnostic Accuracy in Medical Imaging: A Study on the Efficacy of Convolutional Neural Networks and Transfer Learning in AI-Assisted Radiology

Abstract

This study investigates the potential of convolutional neural networks (CNNs) and transfer learning to enhance diagnostic accuracy in medical imaging, focusing on AI-assisted radiology. The research addresses the critical need to improve diagnostic precision and reduce human error in radiological assessments. We utilized a dataset comprising thousands of labeled medical images across various imaging modalities, including X-rays, MRIs, and CT scans. A CNN architecture was developed and optimized for this purpose, incorporating state-of-the-art techniques such as data augmentation and dropout to mitigate overfitting. Transfer learning was employed to leverage pre-trained models, significantly speeding up the training process and improving generalization capabilities. The CNNs were evaluated against a standard radiological diagnostic benchmark, showing substantial improvements in both sensitivity and specificity. Our results demonstrate a marked increase in diagnostic accuracy, with the AI model outperforming conventional radiological methods. The findings suggest that integrating CNNs with transfer learning in radiological workflows can not only reduce diagnostic errors but also enhance the efficiency of radiologists by providing accurate preliminary assessments. Furthermore, this research underscores the importance of AI in revolutionizing medical diagnostics and offers insights into future applications of machine learning in healthcare.