Abstract: Automatic disease diagnosis using medical imaging has been a hot research topic in the past few years. Over the past decade, significant research efforts have been made in X-ray and CT image analysis and diagnosis of different diseases, including but not limited to laparoscopic surgical actions, kidney stone types, Alzheimer’s disease, and other general diseases like heart problems. Medical imaging is a very helpful and effective tool for the diagnosis of atypical and common symptoms. In recent years, novel and enhanced imaging methods have been developed for the effective extraction of medical images with advanced resolution and other enhanced features. However, although modern imaging modes are advanced and very effective for extraction, the interpretation of these images is still labor-intensive and requires high expertise in the relevant field. There is a growing void between the discovery of images and their interpretation due to the scarcity of expert doctors in this field. The solution is automation, and the best approach to deploy such automation at a grand scale in real life is to utilize AI. Artificial intelligence comprises various fields that assist in tackling tough problems in automation, such as computer vision, natural language processing, and robotics. Among these different fields, computer vision has achieved tremendous success in recent years and is very active in both academia and industry. Numerous intelligent computer vision systems are deployed in different domains, including but not limited to autonomous driving, agriculture, wildlife, security, social media, smart retail, and health care. The healthcare domain is one of the most active computer vision research areas due to automatic medical imaging diagnosis becoming an increasingly attractive research problem. Early automatic diagnosis is essential for providing timely interventions. It is difficult to discover effective workaround solutions for complex processes like human actions, building structure parsing, security event understanding, and so on. But it is comparatively easier to devise solutions. Thus, significant research efforts have been made in medical image analysis and disease diagnosis.
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DOI:
10.17148/IJARCCE.2022.111252
