Effective hybrid deep learning model for COVID-19 patterns identification using CT images.

Coronavirus disease 2019 (COVID-19) has attracted significant attention of researchers from various disciplines since the end of 2019. Although the global epidemic situation is stabilizing due to vaccination, new COVID-19 cases are constantly being discovered around the world. As a result, lung computed tomography (CT) examination, an aggregated identification technique, has been used to ameliorate diagnosis. It helps reveal missed diagnoses due to the ambiguity of nucleic acid polymerase chain reaction. Therefore, this study investigated how quickly and accurately hybrid deep learning (DL) methods can identify infected individuals with COVID-19 on the basis of their lung CT images. In addition, this study proposed a developed system to create a reliable COVID-19 prediction network using various layers starting with the segmentation of the lung CT scan image and ending with disease prediction. The initial step of the system starts with a proposed technique for lung segmentation that relies on a no-threshold histogram-based image segmentation method. Afterward, the GrabCut method was used as a post-segmentation method to enhance segmentation outcomes and avoid over-and under-segmentation problems. Then, three pre-trained models of standard DL methods, including Visual Geometry Group Network, convolutional deep belief network, and high-resolution network, were utilized to extract the most affective features from the segmented images that can help to identify COVID-19. These three described pre-trained models were combined as a new mechanism to increase the system's overall prediction capabilities. A publicly available dataset, namely, COVID-19 CT, was used to test the performance of the proposed model, which obtained a 95% accuracy rate. On the basis of comparison, the proposed model outperformed several state-of-the-art studies. Because of its effectiveness in accurately screening COVID-19 CT images, the developed model will potentially be valuable as an additional diagnostic tool for leading clinical professionals.

Identifying defective solar cells in electroluminescence images using deep feature representations.

Electroluminescence (EL) imaging is a technique for acquiring images of photovoltaic (PV) modules and examining them for surface defects. Analysis of EL images has been manually performed by visual inspection of images by experts. This manual procedure is tedious, time-consuming, subjective, and requires deep expert knowledge. In this work, a hybrid and fully-automated classification system is developed for detecting different types of defects in EL images. The system fuses the deep feature representations extracted from two different deep learning models (Inception-V3 and ResNet50) to form more discriminative feature vectors. These feature vectors are then fed into the classifier layer to assign them into one of different types of defects. A large-scale, challenging solar cells dataset composed of 2,624 EL images was used to assess the performance of the proposed system in both the binary classification (functional vs defective) task and multi-class classification (functional, mild, moderate, and severe) task. The proposed system has managed to detect the correct defect type with less than 1 s per image with an accuracy rate of 98.15% and 95.35% in the binary classification and multi-classification task, respectively.

A modern deep learning framework in robot vision for automated bean leaves diseases detection.

The bean leaves can be affected by several diseases, such as angular leaf spots and bean rust, which can cause big damage to bean crops and decrease their productivity. Thus, treating these diseases in their early stages can improve the quality and quantity of the product. Recently, several robotic frameworks based on image processing and artificial intelligence have been used to treat these diseases in an automated way. However, incorrect diagnosis of the infected leaf can lead to the use of chemical treatments for normal leaf thereby the issue will not be solved, and the process may be costly and harmful. To overcome these issues, a modern deep learning framework in robot vision for the early detection of bean leaves diseases is proposed. The proposed framework is composed of two primary stages, which detect the bean leaves in the input images and diagnosing the diseases within the detected leaves. The U-Net architecture based on a pre-trained ResNet34 encoder is employed for detecting the bean leaves in the input images captured in uncontrolled environmental conditions. In the classification stage, the performance of five diverse deep learning models (e.g., Densenet121, ResNet34, ResNet50, VGG-16, and VGG-19) is assessed accurately to identify the healthiness of bean leaves. The performance of the proposed framework is evaluated using a challenging and extensive dataset composed of 1295 images of three different classes (e.g., Healthy, Angular Leaf Spot, and Bean Rust). In the binary classification task, the best performance is achieved using the Densenet121 model with a CAR of 98.31%, Sensitivity of 99.03%, Specificity of 96.82%, Precision of 98.45%, F1-Score of 98.74%, and AUC of 100%. The higher CAR of 91.01% is obtained using the same model in the multi-classification task, with less than 2 s per image to produce the final decision.

Role of leukotriene inhibitors in the postoperative management of nasal polyps.

This was a prospective double blind comparative study on 40 patients. It compared the effects of the leukotriene receptor antagonist montelukast and beclomethasone nasal spray on the post-operative course of patients with sinonasal polyps. All patients underwent endoscopic sphenoethmoidectomy and were randomized post-operatively into two groups. Group I: 20 patients (9 females and 11 males) age 17 to 67 (32.4 +/- 9.5 years), receiving 10 mg montelukast orally daily and Group II: 20 patients (6 females and 14 males) age 17 years to 57 years (33.5 +/- 11.9 years), receiving 400 ug beclomethasone local sprays daily. All patients were followed up for 1 year and a symptom score was recorded throughout this period. There was a significant reduction in symptom scores in both groups throughout the study period. In the montelukast group improvement was more marked in itching, post-nasal discharge and headache. The control of sneezing and rhinorrhea was comparable in both groups with a marginal advantage of montelukast. Steroids had a more marked effect on smell disturbances and obstruction. There was no difference in the recurrence rate or in the need for rescue medications between both groups. Both drugs seem to have a complementary action and further studies are needed to determine which patients should receive which treatment.