A Deep Learning-based Approach for Medical Image Analysis and Diagnosis

Abstract

The application of deep learning-based methods has revolutionized medical image processing and diagnosis. These methods have shown considerable promise in improving the accuracy and efficiency of medical image processing, reducing the burden on medical staff, and, ultimately, yielding better outcomes for patients. This study aims to summaries the most significant findings from deep learning-based approaches for analyzing and diagnosing medical images. This overview looks at recent literature and describes proposed systems, barriers, and applications of several approaches to this issue. Several barriers have been identified via this analysis, including but not limited to: data quality, data generalizability, data interpretability, ethical and regulatory concerns, integration with clinical workflow, and computer resources. A multidisciplinary approach is necessary to effectively address these challenges; this approach should underline the need of collaboration between researchers, medical professionals, and industry partners. Automated diagnosis, image segmentation, image registration, picture synthesis, and the discovery of biomarkers are just some of the many uses of deep learning-based algorithms in medical image analysis and diagnosis. The field of medical imaging stands to benefit greatly from deep learning-based approaches, which have the potential to change the lives of millions of people across the world for the better.