Using deep neural networks to diagnose medical conditions from radiology and MRI images

الملخص

Improving patient outcomes depends on the fast and accurate identification of medical disorders utilising MRI and radiography imaging. Conventional diagnosis mostly depends on the skill of radiologists, whose judgement may be impacted by weariness, workload, and differing levels of experience, which might result in judgements that are delayed or inaccurate. Deep neural networks' (DNNs') application , more Convolutional neural networks (CNNs) in particular , to the examination of MRI and X-ray images and the very accurate identification of medical anomalies is examined in this work. 500 normal MRI scans, 300 tumor-containing MRI scans, 400 normal X-ray pictures, and 200 X-ray images with pneumonia were gathered from hospital archives and public databases. In order to enhance the model's generalization , preprocessing and information augmentation were utilized. When evaluated using accuracy, precision, recall, and F1-score, the CNN model performed comparably to experienced radiologists; in most cases, the machine's performance was somewhat better than that of a human. The results demonstrate that CNNs can reliably support clinical decision-making, reduce diagnostic load, and accurately identify a broad spectrum of medical issues. These results demonstrate how deep learning may be used to enhance conventional diagnostic tools and point to directions for further study, such as multi-class categorization , combining deep learning with other AI approaches, and practical application.