ABSTRACT
We live in the age of algorithms, machine learning (ML), and deep learning (DL) systems, which are transforming industries such as manufacturing, transportation, and management. DL improves performance in multiple areas, such as computer vision, text analysis, and speech. Deal with over time. Due to the extensive use of machine learning and machine learning algorithms in many fields, these technologies are inseparable from our daily lives. Healthcare is now being influenced by the ML/DL algorithm. Artificial intelligence (AI), ML, and DL have been steadily infiltrating the medical industry over the past few years.
Now consider that we live remotely from the hospital, we don't have adequate money to pay the hospital bill, or we don't have ample time to start. In such cases, diagnosing the disease with sophisticated equipment will save lives. Scientists have developed AI/DL-based diagnosis algorithms to diagnose a range of diseases, including rheumatoid arthritis, cancer, lung disease, heart disease, diabetic retinopathy, hepatitis, Alzheimer's disease, liver disease, dengue fever, and Parkinson's disease. Various ML/DL algorithms have been investigated by researchers to diagnose diseases. The ML/DL algorithm has been approved by researchers for use in diagnosing a variety of diseases.
However, security and privacy are two big concerns in 5G-enabled Healthcare Informatics. Not only is it seen as having the potential to damage the monetary penalty, but it also creates much more serious issues, such as consumer confidence, social trust, and personal safety.
The main DL strategies used in 5G scenarios as well as the most common scenarios used to evaluate 5G and DL integration are covered in this chapter. Its aim is to examine the use of DL in the effective diagnosis of disease risk factors, thus assisting medical professionals in making specific decisions. Three separate case studies are also explored in detail to explain the importance of DL techniques to disease diagnosis. This chapter focuses on recent advances in DL that have had a significant impact on disease identification and treatment.
