Deep Learning Applications in Medical Image Analysis:Enhancing Radiology with Automated Diagnostic Tools

Abstract

The main objective of this study is to incorporate deep learning–based automated tools into radiological diagnostics to improve diagnostic accuracy, decrease human error with the aid of computer algorithms, and increase workflow efficiency. The main goal is to evaluate the utility of such tools for detection and classification in practical radiological workflows, helping radiologists provide even faster and more reliable diagnoses.

To establish a thorough methodology, data preprocessing, model selection, and validation were applied for a variety of medical imaging datasets such as X-rays, computed tomography (CT), magnetic resonance imaging (MRI) etc. These datasets were then used to train deep learning models, mainly convolutional neural networks (CNNs) and transformer architectures to identify feature determinants for common diagnostic markers. Various imaging modalities are being compared based on performance metrics accuracy, sensitivity and specificity. A comparison to more conventional methods of radiology was also performed to demonstrate the relative strengths and weaknesses of deep learning models.

Findings showed that deep learning algorithms may enhance diagnostic accuracy in medical imaging, attaining higher levels of sensitivity and specificity than standard approaches in some examples. It also found that automated diagnostics could save time for image analysis, improving workflow and enabling radiologists to focus on challenging cases. Error analysis identified components requiring further development, especially on edge cases and rare disease detection where traditional and deep learning methods may work best in combination.