BEMD-3DCNN-based method for COVID-19 detection

High-accuracy heart rate detection using multispectral IPPG technology combined with a deep learning algorithm

Image Photoplethysmography (IPPG) technology is a noncontact physiological parameter detection technology, which has been widely used in heart rate (HR) detection. However, traditional imaging devices still have issues such as narrower receiving spectral range and inferior motion detection performance. In this paper, we propose a HR detection method based on multi-spectral video. Our method combining multispectral imaging with IPPG technology provides more accurate physiological information. To realize real-time evaluation of HR directly from facial multispectral videos, we propose a new end-to-end neural network, namely IPPGResNet18. The IPPGResNet18 model was trained on the multispectral video dataset from which better results were achieved: MAE = 2.793, RMSE = 3.695, SD = 3.707, p = 0.304. The experimental results demonstrate a high accuracy of HR detection under motion state using this detection method. In respect of real-time monitoring of HR during movement, our method is obviously superior to the conventional technical solutions.

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

Deep Convolutional Neural Networks for Detecting COVID-19 Using Medical Images: A Survey

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease.

EOS-3D-DCNN: Ebola optimization search-based 3D-dense convolutional neural network for corn leaf disease prediction

Corn disease prediction is an essential part of agricultural productivity. This paper presents a novel 3D-dense convolutional neural network (3D-DCNN) optimized using the Ebola optimization search (EOS) algorithm to predict corn disease targeting the increased prediction accuracy than the conventional AI methods. Since the dataset samples are generally insufficient, the paper uses some preliminary pre-processing approaches to increase the sample set and improve the samples for corn disease. The Ebola optimization search (EOS) technique is used to reduce the classification errors of the 3D-CNN approach. As an outcome, the corn disease is predicted and classified accurately and more effectually. The accuracy of the proposed 3D-DCNN-EOS model is improved, and some necessary baseline tests are performed to project the efficacy of the anticipated model. The simulation is performed in the MATLAB 2020a environment, and the outcomes specify the significance of the proposed model over other approaches. The feature representation of the input data is learned effectually to trigger the model's performance. When the proposed method is compared to other existing techniques, it outperforms them in terms of precision, the area under receiver operating characteristics (AUC), f1 score, Kappa statistic error (KSE), accuracy, root mean square error value (RMSE), and recall.

A Survey on Deep-Learning-Based Diabetic Retinopathy Classification

The number of people who suffer from diabetes in the world has been considerably increasing recently. It affects people of all ages. People who have had diabetes for a long time are affected by a condition called Diabetic Retinopathy (DR), which damages the eyes. Automatic detection using new technologies for early detection can help avoid complications such as the loss of vision. Currently, with the development of Artificial Intelligence (AI) techniques, especially Deep Learning (DL), DL-based methods are widely preferred for developing DR detection systems. For this purpose, this study surveyed the existing literature on diabetic retinopathy diagnoses from fundus images using deep learning and provides a brief description of the current DL techniques that are used by researchers in this field. After that, this study lists some of the commonly used datasets. This is followed by a performance comparison of these reviewed methods with respect to some commonly used metrics in computer vision tasks.

ViDMASK dataset for face mask detection with social distance measurement

The COVID-19 outbreak has extenuated the need for a monitoring system that can monitor face mask adherence and social distancing with the use of AI. With the existing video surveillance systems as base, a deep learning model is proposed for mask detection and social distance measurement. State-of-the-art object detection and recognition models such as Mask RCNN, YOLOv4, YOLOv5, and YOLOR were trained for mask detection and evaluated on the existing datasets and on a newly proposed video mask detection dataset the ViDMASK. The obtained results achieved a comparatively high mean average precision of 92.4% for YOLOR. After mask detection, the distance between people's faces is measured for high risk and low risk distance. Furthermore, the new large-scale mask dataset from videos named ViDMASK diversifies the subjects in terms of pose, environment, quality of image, and versatile subject characteristics, producing a challenging dataset. The tested models succeed in detecting the face masks with high performance on the existing dataset, MOXA. However, with the VIDMASK dataset, the performance of most models are less accurate because of the complexity of the dataset and the number of people in each scene. The link to ViDMask dataset and the base codes are available at https://github.com/ViDMask/VidMask-code.git.