Investigation of effective climatology parameters on COVID-19 outbreak in Iran.

Spatial analysis and GIS in the study of COVID-19. A review.

Assessing the relationship between surface levels of PM2.5 and PM10 particulate matter impact on COVID-19 in Milan, Italy.

Evidence that high temperatures and intermediate relative humidity might favor the spread of COVID-19 in tropical climate: A case study for the most affected Brazilian cities.

Relationship between COVID-19 and weather: Case study in a tropical country.

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical applications, such as postoperative assessment, surgical planning, and pathological diagnosis of hepatic diseases. However, there are still a considerable number of difficulties to overcome due to the fuzzy boundary, irregular shapes, and complex tissues of the liver. In this paper, for liver and tumor segmentation and to overcome the mentioned challenges a simple but powerful strategy is presented based on a cascade convolutional neural network. At the first, the input image is normalized using the Z-Score algorithm. This normalized image provides more information about the boundary of tumor and liver. Also, the Local Direction of Gradient (LDOG) which is a novel encoding algorithm is proposed to demonstrate some key features inside the image. The proposed encoding image is highly effective in recognizing the border of liver, even in the regions close to the touching organs. Then, a cascade CNN structure for extracting both local and semi-global features is used which utilized the original image and two other obtained images as the input data. Rather than using a complex deep CNN model with a lot of hyperparameters, we employ a simple but effective model to decrease the train and testing time. Our technique outperforms the state-of-the-art works in terms of segmentation accuracy and efficiency.

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Gastric cancer (GC) is the third reason for cancer-related deaths in the world. The late referral of patients to medical centers in an advanced stage can make the treatment procedure more difficult. Accurate diagnosis of risk factors in GC tumor size and tumor location can lead to taking preventive measures or determining a suitable treatment strategy. This study aims to present a general model to identify the correlation of different parameters in a GC tumor place and tumor size. The medical documents of GC patients consist of the dataset of this study. The effect of seven main parameters, namely age, smoking, Helicobacter pylori (H. pylori) infection, job, surgical background, sex, and nodal stage is investigated in GC tumor location and tumor size. By considering all the medical documents, data modeling is conducted using gene expression programming because of the high precision of model output. In the following, three different sensitivity analysis methods (Morris, Distributed Evaluation of Local Sensitivity Analysis (DELSA), and Sobol’–Jansen) are applied to determine the influential factors in the tumor size and location. Results show that in sequence, sex, age, and H. pylori records mostly affect tumor location; the nodal stage, smoking, and surgery record mostly affect tumor size. This method can help in identifying effective parameters and prevention of patients’ death in all types of diseases, even for terminal illnesses.

Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location

Abstract The increased intracranial pressure (ICP) can be described as an increase in pressure around the brain and can lead to serious health problems. The assessment of ultrasound images is commonly conducted by skilled experts which is a time-consuming approach, but advanced computer-aided diagnosis (CAD) systems can assist the physician to decrease the time of ICP diagnosis. The accurate detection of the nerve optic regions, with drawing a precise slope line behind the eyeball and calculating the diameter of nerve optic, are the main aims of this research. First, the Fuzzy C-mean (FCM) clustering is employed for segmenting the input CT screening images into the different parts. Second, a histogram equalization approach is used for region-based image quality enhancement. Then, the Local Directional Number method (LDN) is used for representing some key information in a new image. Finally, a cascade Convolutional Neural Network (CNN) is employed for nerve optic segmentation by two distinct input images. Comprehensive experiments on the CT screening dataset [The Cancer Imaging Archive (TCIA)] consisting of 1600 images show the competitive results of inaccurate extraction of the brain features. Also, the indexes such as Dice, Specificity, and Precision for the proposed approach are reported 87.7%, 91.3%, and 90.1%, respectively. The final classification results show that the proposed approach effectively and accurately detects the nerve optic and its diameter in comparison with the other methods. Therefore, this method can be used for early diagnose of ICP and preventing the occurrence of serious health problems in patients. 

https://link.springer.com/content/pdf/10.1007/s40747-022-00694-w.pdf

Shadi Dorosti

Papers

Investigation of effective climatology parameters on COVID-19 outbreak in Iran.

TPCNN: Two-path convolutional neural network for tumor and liver segmentation in CT images using a novel encoding approach

Application of gene expression programming and sensitivity analyses in analyzing effective parameters in gastric cancer tumor size and location

Nerve optic segmentation in CT images using a deep learning model and a texture descriptor

Patient waiting time management through fuzzy based failure mode and effect analysis