Detection of Diabetic Retinopathy Based on Convolutional Neural Networks: A Review

A major cause of human vision loss worldwide is Diabetic retinopathy (DR). The disease requires early screening for slowing down the progress. However, in low-resource settings where few ophthalmologists are available to care for all patients with diabetes, the clinical diagnosis of DR will be a considerable challenge. This paper, review the most recent studies on the detection of DR by using one of the efficient algorithms of deep learning, which is Convolutional Neural Networks (CNN), which highly used to detect DR features from retinal images. CNNs approach to DR detection saves time and expense, and is more efficient and accurate than manual diagnostics. Therefore, CNN is essential and beneficial for DR detection.

The role of manual therapy in complex medical rehabilitation

In recent years, manual therapy has been increasingly used. This was facilitated by the scientific justification of the method, wellestablished training of specialists, good results of manual diagnostics and therapy of patients. Manual medicine is a system of diagnostic and therapeutic manual techniques aimed at identifying and treating disorders of the musculoskeletal system, manifested in the form of functional joint blockages, hypermobility and regional postural muscle imbalance. In addition to nosological diagnosis, manual examination data are of great importance for successful treatment. Manual diagnostics is a method aimed at detecting disorders in the spine, muscles and joints, as well as ligaments of the human body. The main task of diagnostics is to establish a differential diagnosis between diseases of the spine and joints and other nosological forms. Timely manual diagnostics with the use of manual therapy technologies helps to increase the effectiveness of complex treatment and helps to avoid complications and disability at an early age.