Enhancing Diagnostic Accuracy in Medical Imaging through Convolutional Neural Networks and Transfer Learning Algorithms

Abstract

This research paper explores the enhancement of diagnostic accuracy in medical imaging by leveraging convolutional neural networks (CNNs) and transfer learning algorithms. It begins by addressing the inherent challenges faced in medical imaging, such as variability in image acquisition, complex tissue structures, and the need for precise diagnosis. The application of CNNs, known for their efficacy in image classification and pattern recognition, is examined in this context. The study implements various CNN architectures to assess their performance in improving diagnostic outcomes across different imaging modalities, including MRI, CT scans, and X-rays. To mitigate the requirement for extensive labeled datasets, transfer learning techniques are employed to adapt pre-trained CNN models, significantly reducing computational resources and training time. The paper presents a comparative analysis of CNN architectures with and without transfer learning, evaluated on multiple benchmark datasets. Experimental results demonstrate a marked improvement in accuracy, sensitivity, and specificity when employing transfer learning approaches, particularly in cases with limited data availability. Furthermore, the findings underscore the potential of these advanced algorithms to support radiologists and clinicians, leading to more reliable and quicker diagnosis. The conclusions highlight the transformative impact of integrating CNNs with transfer learning in clinical practice and suggest pathways for future research, including the integration of multimodal data and the development of specialized models for rare pathologies.