Background
Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, and , are investigated for semantically segmenting infected tissue regions in CT lung images.
Methods
We propose to use two known deep learning networks, and , for image tissue classification. is characterized as a scene segmentation network and as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly.
Results
The results show the superior ability of in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the shows better results as a multi-class segmentor (with 0.91 mean accuracy).
Conclusion
Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.
Sensory evaluation is often the ultimate measure of food quality. but food process control relies on instrumental measurements. Ejrective techniques are needed to convert desired sensory quality targets into instrumental process set points. This paper describes techniques developed for determining instrumental process set points from sensory evaluations. Various cases and different approaches depending on the nature of the sensory-instrumental relationships are outlined. The major issues addressed include additional constraints for underdetermined cases and reverse mapping with neural networks for nonlinear multivariate cases. These techniques were illustrated and tested with experimental data based on wa@e samples. Seven sensory attributes were evaluated by trained panelists and instrumental measurements were obtained with a color computer vision system. For nonlinear multivariate cases, reverse mapping with neural networks successfully mapped sensory measurements to instrumental process set points with average errors less than I . 3 SQ . The results demonstrate the eflectiveness of the techniques developed.' Corresponding author. TEL (573) 882-7778; FAX: (573) 884-5650; EMAIL: TanJ@missouri.edu
Color and geometric characteristics of stained areas in histochemical slides are among the features pathologists assess to evaluate the severity of lesions. In this research, image processing techniques were used to perform objective quantification of these characteristics in images of H&E-stained spleen tissues. A segmentation algorithm was developed to isolate the areas of interest in microscopic tissue images. Image features important to pathological evaluation were then extracted. These features were used to build statistical and neural network models to predict pathologist scores. A linear regression model predicted the scores to an R(2)-value of 0.6, and a neural network model classified samples to an accuracy of 75%. The results show the usefulness of image processing as a tool for pathological evaluation.
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