Medical imaging is a crucial component of contemporary healthcare, as it makes it easier to identify and diagnose a variety of health issues, including cancers, fractures, tumors and infections. But to take a note manually analyzing medical pictures like CT, MRI, and X-rays is a laboriously difficult process that mostly relies on the knowledge of medical specialists. And thereafter , delays in medical diagnosis and an increase in decision-making errors have been brought on by the growing number of medical images and the lack of specialists available. With this regard, this research indicates tht a deep learning-based artificial intelligence (AI) system for medical picture diagnosis can be simulated. Convolutional Neural Networks (CNNs) as the foundation of this system, are capable of learning characteristics from medical images and categorizing them into as normal or pathological. A medical image diagnosis system that accepts to a particular framework comprising picture acquisition, preprocessing, feature learning, classification, and visualization is attempted to be intended in this research. Employing a number of preprocessing methods, including scaling, normalization, and noise reduction, the system enhances image quality. As a result, the model performs far better. And furthermore, the findings of the research show that this system may achieve high accuracy and lower the time needed for medical diagnostics. The technology has a goal to help medical personnel or staff make judgments more quickly and accurately by automating the study of medical pictures/images. Additionally, the technology might be embodied into a cloud-based platform to facilitate remote medical diagnosis, especially in rural areas, thereby improving and scaling up medical care accessibility.
Garud et al. (Thu,) studied this question.