Deep Learning Techniques for Medical Image Analysis and Diagnosis
Keywords:
Deep Learning, Medical Image Analysis, Medical Imaging, Diagnosis, Convolutional Neural Networks (CNN), Image Recognition.Abstract
The medical field has the most potential to improve care standards through new technologies because it is always evolving. In order to diagnose and classify physiological anomalies and ensure that patients receive the best care possible, most medical specializations are evolving through the application of numerous state-of-the-art technologies. To evaluate the middle image and pinpoint the exact problem, the images taken in the affected area are subjected to an image processing technique. It might possibly provide a possible answer to problems. Many deep learning technologies are being used efficiently in almost all areas of medical therapy. We evaluate the use of deep learning for tasks such as object identification, segmentation, registration, and picture classification, and we offer brief descriptions of research for each application area. Prospects for future investigation and unresolved issues are examined. This study accurately predicts diabetes in patients using deep learning and machine learning algorithms. To balance the uneven dataset, the researchers employed five distinct machine learning techniques, feature engineering, and SMOTE analysis. In order to ensure accurate diabetes prediction, they also did hyper-parameter tweaking on the validation data. The goal of the project is to expedite the timely diagnosis of diabetes, a fatal illness that affects around 500 million people worldwide.
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