Disease detection rates are not necessarily a good way to evaluate a disease detection method in a longitudinal study

2011 ◽  
Vol 21 (eLetters Supplement) ◽  
Author(s):  
Jacob ED Levman
Keyword(s):  
Longitudinal Study ◽  
Detection Method ◽  
Disease Detection ◽  
Detection Rates

scholarly journals Spread and Increase of Ratoon Stunting Disease of Sugarcane and Comparison of Disease Detection Methods

Plant Disease ◽  
1999 ◽  
Vol 83 (12) ◽  
pp. 1170-1175 ◽  
Author(s):  
J. W. Hoy ◽  
M. P. Grisham ◽  
K. E. Damann
Keyword(s):  
Yield Loss ◽  
Detection Method ◽  
Xylem Sap ◽  
Disease Spread ◽  
Detection Methods ◽  
Disease Detection ◽  
Correct Identification ◽  
Dot Blot ◽  
Ratoon Stunting Disease ◽  
Enzyme Immunoassays

The spread and increase of ratoon stunting disease (RSD) resulting from two mechanical harvests were compared in eight sugarcane cultivars at two locations. RSD spread and increase were detected in the ratoon crops grown after each harvest and varied among cultivars and locations. Disease spread and increase were greater in plants grown from stalks collected at the first harvest than in the first ratoon growth from the harvested field. RSD infection was determined using five disease detection methods: alkaline-induced metaxylem autofluorescence; microscopic examination of xylem sap; and dot blot, evaporative-binding, and tissue blot enzyme immunoassays. The tissue blot enzyme immunoassay was the most accurate RSD detection method. The dot blot and evaporative-binding enzyme immunoassays were the least sensitive for detection of RSD-infected stalks, and alkaline-induced metaxylem autofluorescence was least accurate for correct identification of noninfected stalks. The results indicate that disease spread and increase are variable even among cultivars susceptible to yield loss due to RSD, and the greatest threat of disease spread and increase occurs at planting.

Cassava Disease Detection Method Based on EfficientNet

2021 ◽  
Author(s):  
Fei Gao ◽  
Jiming Sa ◽  
Zhuoer Wang ◽  
Zhongyu Zhao
Keyword(s):  
Detection Method ◽  
Disease Detection

An Image Hided-Data Detection Method Combining Markov Chain and Support Vector Machines

2011 ◽  
Vol 128-129 ◽  
pp. 520-524
Author(s):  
Hui Min Zhao ◽  
Li Zhu
Keyword(s):  
Markov Chain ◽  
Support Vector Machines ◽  
Spread Spectrum ◽  
Detection Method ◽  
Markov Chain Model ◽  
Support Vector ◽  
Data Detection ◽  
Chain Model ◽  
Detection Rates ◽  
Vector Machines

An image hided-data detection method is proposed combining 2-D Markov chain model and Support Vector Machines (SVM) by the paper, in which image pixels are predicted with their neighboring pixels, and the prediction-error image is generated by subtracting the prediction value from the pixel value. Support vector machines are utilized as classifier. As embedding data rate being 0.1 bpp, experimental investigation utilizing spread spectrum (SS) and a Quantization Index Modulation (QIM) method data hiding method respectively , correction detection rates are all above 90% . For optimum LSB method ,the method achieves a detection rate from 50% to 90% above with 0.01bpp-0.3bpp various embedding data rates.

Intrusion Detection Technique Based on Improved Intuitionistic Fuzzy Neural Networks

2015 ◽  
Vol 713-715 ◽  
pp. 2507-2510
Author(s):  
Yang Lei ◽  
Jing Ma
Keyword(s):  
Neural Networks ◽  
Intrusion Detection ◽  
Computer Security ◽  
Detection Method ◽  
Fuzzy Neural Networks ◽  
Detection Methods ◽  
Detection Rates ◽  
Intuitionistic Fuzzy ◽  
Sufficient Stability ◽  
Fuzzy Neural

At present, the issue of intrusion detection has been a hot point to all over the computer security area. In this paper, a novel intrusion detection method has been proposed. Unlike the current existent detection methods, this paper combines the theories of both intuitionistic fuzzy sets (IFS) and artificial neural networks (ANN) together, which leads to much fewer iteration numbers, higher detection rates and sufficient stability. Experimental results show that the now method proposed in this paper is promising and has obvious superiorities over other current typical ones.

scholarly journals Novel Multi Center and Threshold Ternary Pattern Based Method for Disease Detection Method Using Voice

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 84532-84540 ◽  
Author(s):  
Turker Tuncer ◽  
Sengul Dogan ◽  
Fatih Ozyurt ◽  
Samir Brahim Belhaouari ◽  
Halima Bensmail
Keyword(s):  
Detection Method ◽  
Disease Detection

Computer-Aided Interpretation of Coronary Cineangiograms

1992 ◽  
Vol 33 (1) ◽  
pp. 6-9 ◽  
Author(s):  
T. Sugahara ◽  
Y. Yamagihara ◽  
N. Sugimoto ◽  
K. Kimura ◽  
K. Awano ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Image ◽  
Detection Rate ◽  
Detection Method ◽  
Visual Interpretation ◽  
Detection Rates ◽  
Computer Image Processing ◽  
Computer Aided ◽  
Computer Recognition ◽  
Stenotic Lesions

To accurately diagnose stenotic lesions on coronary cineangiograms, an automatic detection method using computer image processing was developed. We evaluated its accuracy by comparing the results of computer-aided interpretation (CAI) with those obtained independently by 3 observers. Evaluation was performed on 129 segments from 27 arteries visualized on angiograms obtained in 18 patients. The detection rates of stenosis of the 3 observers by pure visual interpretation were 7.0%, 27.9%, and 17.1%, and using CAI 40.0%, 42.6%, and 47.3%. By computer recognition alone, a detection rate of 51.9% was achieved. The agreement by at least 2 observers (consensus) on the sites with lesions was 41.1% while the consensus of computer recognition regarding the sites with lesion was 40.3%. Therefore, our findings indicated that computer recognition of cineangiograms is likely to result in overdetection of lesions. However, all 3 observers detected stenotic lesions better with CAI than with pure visual interpretation. Accordingly, CAI may improve the reliability of cineangiographic diagnosis.

scholarly journals A Valuable Antigen Detection Method for Diagnosis of Acute Hepatitis E

2014 ◽  
Vol 53 (3) ◽  
pp. 782-788 ◽  
Author(s):  
Gui-Ping Wen ◽  
Zi-Min Tang ◽  
Fan Yang ◽  
Ke Zhang ◽  
Wen-Fang Ji ◽  
...  
Keyword(s):  
Acute Hepatitis ◽  
Detection Method ◽  
Public Health Problem ◽  
Hepatitis E ◽  
Antigen Detection ◽  
Enzyme Linked Immunosorbent Assay ◽  
Serum Samples ◽  
Detection Rates ◽  
Acute Hepatitis E ◽  
Capture Antibody

Hepatitis E virus (HEV) is a serious public health problem. The commonly used tests that are specific for current HEV infection diagnosis include the detection of anti-HEV IgM and HEV RNA. Here, we report an improved enzyme-linked immunosorbent assay (ELISA) method for HEV antigen detection with a linear range equivalent to 6.3 × 103to 9.2 × 105RNA copies per ml. The monoclonal antibody (MAb) 12F12, a high-ability MAb that binds HEV virus, was selected as the capture antibody from a panel of 95 MAbs. The positive period of HEV antigenemia in infected monkeys using this test was, on average, 3 weeks longer than previously reported and covered the majority of the acute phase. The positive detection rates of IgM, RNA, and new antigen from the first serum samples collected from 16 confirmed acute hepatitis E patients were 81% (13/16), 81% (13/16), and 100% (16/16), respectively. In three patients, the initial serum specimens that tested negative for IgM, despite the presence of symptoms of acute hepatitis and elevated alanine aminotransferase (ALT) levels, were positive for HEV antigen and HEV RNA. In contrast, the serum samples of the three RNA-negative patients were antigen positive (and IgM positive), possibly due to the degradation of HEV nucleic acids. Our results suggest that this new antigen detection method has acceptable concordance with RNA detection and could serve as an important tool for diagnosing acute hepatitis E.

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