Diagnosing COVID-19 disease using an efficient CAD system

Todays, COVID-19 has caused much death and its spreading speed is increasing, regarding virus mutation. This outbreak warns diagnosing infected people is an important issue. So, in this research, a computer-aided diagnosis (CAD) system called COV-CAD is proposed for diagnosing COVID-19 disease. This COV-CAD system is created by a feature extractor, a classification method, and a content-based imaged retrieval (CBIR) system. The proposed feature extractor is created by using the modified AlexNet CNN. The first modification changes ReLU activation functions to LeakyReLU for increasing efficiency. The second change is converting a fully connected (FC) layer of AlexNet CNN with a new FC, which results in reducing learnable parameters and training time. Another FC layer with dimensions 1 × 64 is added at the end of the feature extractor as the feature vector. In the classification section, a new classification method is defined in which the majority voting technique is applied on outputs of CBIR, SVM, KNN, and Random Forest for final diagnosing. Furthermore, in retrieval section, the proposed method uses CBIR because of its ability to retrieve the most similar images to the image of a patient. Since this feature helps physicians to find the most similar cases, they could conduct further statistical evaluations on profiles of similar patients. The system has been evaluated by accuracy, sensitivity, specificity, F1-score, and mean average precision and its accuracy for CT and X-ray datasets is 93.20% and 99.38%, respectively. The results demonstrate that the proposed method is more efficient than other similar studies

A new method for image classification and image retrieval using convolutional neural networks

This article proposes a new method for image classification and image retrieval. The advantages of the proposed method are its high performance and requiring less memory compared to other methods. In order to extract image features, a Convolutional Neural Network (CNN), AlexNet, has been used. For image classification, we design a committee of four classifiers trained on graphics cards, narrowing the gap to human performance. For image retrieval, the similarity between extracted features from dataset images and features of the query image is calculated and the final results are visualized. Comprehensive experiments on Corel-1k, Corel-10k, Caltech-101 object and Scene-67 datasets have been investigated to find optimal parameters of the proposed method. The experiments demonstrate the high performance of the proposed method in comparison with the state-of-the-art in the field

A fast and yet efficient YOLOv3 for blood cell detection

These days, blood cell detection in microscopic images plays a vital role in cognition, the health of a patient. Since disease detection based on manual checking of blood cells is mostly time-consuming and full of errors, analysis of blood cells using object detectors can be considered as an effective tool. Hence, in this study, an object detector has been proposed which is used for detecting blood objects such as white blood cells, red blood cells, and platelets. This detector is called FED (Fast and Efficient YOLOv3) and it is a One-Stage detector, which is similar to YOLOv3, performs detection in three scales. For the purpose of increasing efficiency and flexibility, the proposed object detector utilizes the EfficientNet Convolutional Neural Network as the backbone effectiveness. Furthermore, the Dilated Convolution is indeed applied in order to increase receptive view of the backbone. In addition, the Depthwise Separable Convolution method is utilized to minimize the detector's parametersand the Distance Intersection over Union is further used for bounding box regression. Besides, for increasing the performance, the Swish activation function is employed. The experiments are run on the BCCD dataset that the average precision of platelets, red blood cells, and white blood cells become 90.25%, 80.41%, and 98.92%, respectively. The results of experiments and comparisons demonstrate that the proposed FED detector is more efficient than other existing studies for blood cell detection

WAKO - ein Programm zur Berechnung von Komplexionsgleichgewichten in waesserigen Loesungen - Einfuehrung und Beispiel. Protonenverbrauch durch Silikatverwitterung in norddeutschen Loessboeden. Indol-3-Essigsaeure - moegliche Strukturen und Metabolismus

TIB: RO 4548(16) / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekSIGLEDEGerman

Chemoprevention of DMH-induced early colon carcinogenesis in male balb/c mice by administration of lactobacillus paracasei dta81

We evaluated the effects of the probiotic candidate Lactobacillus paracasei DTA81 (DTA81) on liver oxidative stress, colonic cytokine profile, and gut microbiota in mice with induced early colon carcinogenesis (CRC) by 1,2-dimethylhydrazine (DMH). Animals were divided into four different groups (n = 6) and received the following treatments via orogastric gavage for 8 weeks: Group skim milk (GSM): 300 mg/freeze-dried skim milk/day; Group L. paracasei DTA81 (DTA81): 3 × 109 colony-forming units (CFU)/day; Group Lactobacillus rhamnosus GG (LGG): 3 × 109 CFU/day; Group non-intervention (GNI): 0.1 mL/water/day. A single DMH dose (20 mg/kg body weight) was injected intraperitoneally (i.p), weekly, in all animals (seven applications in total). At the end of the experimental period, DTA81 intake reduced hepatic levels of carbonyl protein and malondialdehyde (MDA). Moreover, low levels of the pro-inflammatory cytokines Interleukin-6 (IL-6) and IL-17, as well as a reduced expression level of the proliferating cell nuclear antigen (PCNA) were observed in colonic homogenates. Lastly, animals who received DTA81 showed an intestinal enrichment of the genus Ruminiclostridium and increased concentrations of caecal acetic acid and total short-chain fatty acids. In conclusion, this study indicates that the administration of the probiotic candidate DTA81 can have beneficial effects on the initial stages of CRC development