Medical Diagnosis using Deep Learning Techniques: A Research Survey

The healthcare industry is very different from other industries. It is a high-priority industry and people expect the most significant level of care and services regardless of the expenses they have to pay for it. It didn’t accomplish social acceptance despite the fact that it burns through a high level of the budget. In usual cases, the interpretations of medical information are being done by a medical expert. As far as a medical diagnosis by human experts, it is very restricted because of its subjectivity, the complexity of the disease itself, and broad varieties that exist across various interpretations. After the achievement of deep learning in other medical applications, it is likewise providing exciting solutions with great precision for medical diagnosis and is viewed as a critical method for future applications in the healthcare area. Disease prediction is one of the basic tasks while designing medical diagnosis software. Artificial intelligence and neural networks are two significant procedures that are now used to tackle this kind of medical diagnosis issue. As of late, deep learning strategies have been effectively used in different applications to aid medical diagnosis. It is an easy and on-time measure for patients to examine the disease dependent on clinical and laboratory symptoms with accurate information and provide more effective outcomes for specific illnesses. Subsequently, to decide the application of DL to improve the diagnosis in different medical disciplines, a systematic survey is directed in this research. To do the survey, numerous strategies and parameters are chosen. We additionally present the different deep learning algorithms utilized throughout the years to analyze different diseases and provide a precise diagnosis. The results of this research show the importance of deep learning techniques in medical disciplines. Based on our overview, we present future research topics that could be utilized to lead further research