Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pr...
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Several pests feed on leaves,stems,bases,and the entire plant,causing plant *** a result,it is vital to identify and eliminate the disease before causing any damage to *** detecting plant disease and treating it is pretty challenging in this *** processing is employed to detect plant disease since it requires much effort and an extended processing *** main goal of this study is to discover the disease that affects the plants by creating an image processing system that can recognize and classify four different forms of plant diseases,including Phytophthora infestans,Fusarium graminearum,Puccinia graminis,tomato yellow leaf ***,this work uses the Support vector machine(SVM)classifier to detect and classify the plant disease using various steps like image acquisition,Pre-processing,Segmentation,feature extraction,and *** gray level co-occurrence matrix(glcm)and the local binary pattern features(LBP)are used to identify the disease-affected portion of the plant *** to experimental data,the proposed technology can correctly detect and diagnose plant sickness with a 97.2 percent accuracy.
Increasing fat tissue of obese people, increases the rate of cardiovascular disease, diabetes, metabolic syndromes and dyslipidemia. An increase in the focal tissue of pancreas is a known risk factor of these diseases...
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Increasing fat tissue of obese people, increases the rate of cardiovascular disease, diabetes, metabolic syndromes and dyslipidemia. An increase in the focal tissue of pancreas is a known risk factor of these diseases. Although there exists sufficient research on the diagnosis and treatment of pancreatic cancer, studies have been done on fatty pancreas. In this study, based on ultrasound imaging and using a texture characteristic of glcm, fatty pancreas was divided into three categories: mild, moderate and severe. We compared and analyzed the three groups was by Pancreatic ultrasonography and body characteristics, serological tests, pressure and the degree of arteriosclerosis, against normal control group. The following parameters of control and test groups were measured: WC (waist circumference), BMI (body mass index), TC (total cholesterol), TG (triglyceride), HDL-C (High-density lipoprotein cholesterol) and LDL-C (Low-density lipoprotein cholesterol), SBP (systolic blood pressure), BST (Blood Sugar Test) and aortic PWV (pulse wave velocity). We observed the values correspondingly increasing fat deposition. However, ABI (Ankle Brachial pressure index) stenosis and HDL-C levels decreased with increasing fat deposit (p < 0.05);a drop in these parameters are known to be harmful to the human body. The difference in texture characteristics between normal control group and pancreatic fatty group (mild, moderate, and severe) was statistically confirmed. Ultrasound imaging of pancreatic steatosis categorized the disease as mild, moderate and severe based on the characteristic texture. In conclusion, we observed on increase in metabolic syndrome, dyslipidemia, and arteriosclerosis, proportional to the degree of pancreatic fat deposition. The escalation of these diseases was confirmed and was directly related with predictors of cardiovascular diseases.
Texture refers to the tactile impression, such as rough, silky, bumpy, and other texture terms. The Grey-Level Co-occurrence Matrix (glcm) algorithm is widely used in visual images for texture feature extraction, imag...
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ISBN:
(纸本)9781467376792
Texture refers to the tactile impression, such as rough, silky, bumpy, and other texture terms. The Grey-Level Co-occurrence Matrix (glcm) algorithm is widely used in visual images for texture feature extraction, image structure characterization analysis and texture classification. The glcm can not only give the statistics of pixel gray values occur in an image, but also give multiple characteristics of the images. Since the primate brain, which is constructed with spiking neurons, has excellent performance in terms of image feature extraction, the improved glcm algorithm is used to train a spiking neural network and also to simulate the brain's ability about extract key information and utilize these extracted feature information to classify different texture image. Experimental results in this article show that this combination of the glcm and spiking neural network can effectively extract image features, and the texture classification results is also to achieve satisfactory effect.
The number of expertise's in the medical domain about the breast cancer is limited. Many patients have to wait too long to get their result from the check-up. The system can be used to help patients, students and ...
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The number of expertise's in the medical domain about the breast cancer is limited. Many patients have to wait too long to get their result from the check-up. The system can be used to help patients, students and to decide what cancer type the patient has, what is the stage of the cancer and how it can be recommendation. The experience medical staffs are decreasing in number. When they retired, the new staffs will be replacing their places. So they have to learn many things related to their work. It is to help the expert doctors or medical staffs in their breast cancer diagnosis. The methodology used in the application is Application Development because it promotes the accuracy application development. The application used the 100 data of Breast Cancer for evaluating the gray level co-coordination metric(glcm) algorithm. This dataset is retrieved from Machine Learning. This paper design the technical aspects of some of the query refinement-based medical systems for breast cancer. It also application proposes query refinement- based system for breast cancer knowledge management. The system performance and accuracy are acceptable, with a breast cancer classification accuracy 93%.
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