Systemic treatment of diabetic rats with a novel human growth factor, vascular endothelial growth factor (VEGF)-A165b, reversed neuropathic pain and peripheral nerve damage.
Diabetes has recently emerged as a worldwide problem, and diabetic retinopathy is an abnormal state associated with the human retina. Due to the increase in daily screen-related activities of modern human beings, diabetic retinopathy is more prevalent among adults, leading to minor and major blindness. Doctors and clinicians are unable to perform early diagnoses due to the large number of patients. To solve this problem, this study introduces a classification model for retinal images that distinguishes between the various stages of diabetic retinopathy. This work involves deploying deep and densely connected networks for retinal image analysis with training from scratch. Dense connections between the convolutional layers of the network are an essential factor to enhance accuracy owing to the deeper supervision between layers. Another factor is the growth rate that further assists our model in learning more sophisticated feature maps regarding retinal images from every stage of the network. We compute the area under the curve, sensitivity, and specificity, particularly for messidor-2 and EyePACS. Compared to existing approaches, our method achieved better results, with an approximate rise rate of 0.01, 0.03, and 0.01, respectively. Therefore, computer-aided programs can help in diagnostic centers as automated detection systems.
Human actions that do not conform to usual behavior are considered as anomalous and such actors are called anomalous entities. Detection of anomalous entities using visual data is a challenging problem in computer vision. This paper presents a new approach to detect anomalous entities in complex situations of examination halls. The proposed method uses a cascade of deep convolutional neural network models. In the first stage, we apply a pretrained model of human pose estimation on frames of videos to extract key feature points of body. Patches extracted from each key point are utilized in the second stage to build a densely connected deep convolutional neural network model for detecting anomalous entities. For experiments we collect a video database of students undertaking examination in a hall. Our results show that the proposed method can detect anomalous entities and warrant unusual behavior with high accuracy.
The retina is an important organ of the human body, with a crucial function in the vision mechanism. A minor disturbance in the retina can cause various abnormalities in the eye, as well as complex retinal diseases such as diabetic retinopathy. To diagnose such diseases in early stages,
many researchers are incorporating machine learning (ML) technique. The combination of medical science with ML improves the healthcare diagnosis systems of hospitals, clinics, and other providers. Recently, AI-based healthcare diagnosis systems assist clinicians in handling more patients in
less time and improves diagnosis accuracy. In this paper, we review cutting-edge AI-based retinal diagnosis technologies. This article also briefly describes the potential of the latest densely connected convolutional networks (DenseNets) to improve the performance of diagnosis systems. Moreover,
this paper focuses on state-of-the-art results from comprehensive investigations in retinal diagnosis and the development of AI-based retinal healthcare diagnosis approaches with deep-learning models.
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