Deep Learning In Medical Imaging: Enhancing Diagnostic Accuracy

Abstract

Deep learning (DL) has also become revolutionized in medical imaging, allowing superior diagnostic capability of a wide variety of imaging streams. Sparked by the challenges posed to the radiology, DL systems are being explored in the means of complementing the traditional interpretations and reshaping the workflows. The research is focused on incorporating secondary evidence in the evaluation of the diagnostic possibilities of DL models in large imaging sectors. This was achieved by the use of the design through utilization of the secondary data derived through peer-reviewed articles of 2016 and 2024. The information was collected using databases like PubMed, Scopus, IEEE Xplore, ScienceDirect and Google Scholar datasets. Comparable authoritative research found that DL algorithms were compared to X-ray, Computed tomography (CT), magnetic resonance imaging (MRI), or ultrasound images and provided measures of diagnostic performance, including sensitivity, specificity, and Area Under the Receiver Operating Characteristic Curve (AUC). The DL approaches outperformed the diagnostic models, and the MRI and CT-based deep-learning models attained the greatest sensitivity and specificity. On the whole, DL was better versus the radiologists in all the tasks in every modality, pneumonia detection as well as tumour segmentation. Besides that, transfer learning led to significant increases in model performance in low-data scenarios, particularly on ultrasound and mammography ones. The findings on the secondary data validate the fact that DL algorithms are highly promising to boost diagnostic accuracy, reduce lesion-to-lesion variability, and facilitate radiological decision-making. With such results, the clinical relevance of DL gets validated, and standardised validation and integration models pop up as a requirement.