Early Detection and Diagnosis of High-Consequence Plant Pests in the United States

2009 ◽  
Author(s):  
Kitty F. Cardwell ◽  
William J. Hoffman
Keyword(s):  
United States ◽  
Early Detection ◽  
The United States ◽  
Plant Pests ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

scholarly journals 3D Printing Realistic Endobronchial Models for Surgical Planning and Simulation

2018 ◽  
Author(s):  
Zhuo Zhao ◽  
Javier Coronel Baracaldo ◽  
Juliana Palacio Varona ◽  
Roberto Rueda-Esteban ◽  
Zion Tsz Ho Tse
Keyword(s):  
United States ◽  
Lung Cancer ◽  
Death Rate ◽  
Surgical Planning ◽  
The United States ◽  
Cancer Society ◽  
Accurate Localization ◽  
Common Cancer ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Lung cancer is the second most common cancer in both men and women globally. More than one million lung cancer cases are diagnosed worldwide each year. The leading cause of cancer death is lung cancer in the United States and worldwide [1]. According to the American Cancer Society, there were an estimated 222,500 new cases of lung cancer and 155,870 deaths from lung cancer in the United States in 2017. Early detection and diagnosis, as well as accurate localization in lung intervention, are the keys to reducing the death rate from lung cancer [1].

Detection of Leukemia Using Image Processing

2018 ◽  
Vol 8 (4) ◽  
pp. 61
Author(s):  
A. Sivasangari ◽  
G. Sasikumar
Keyword(s):  
Image Processing ◽  
Statistical Analysis ◽  
Early Detection ◽  
Visual Inspection ◽  
Causes Of Death ◽  
Cure Rate ◽  
Blood Disorders ◽  
Detection And Diagnosis ◽  
The Moment ◽  
Early Detection And Diagnosis

Leukemia   disease   is one   of    the   leading   causes   of death   among   human. Its  cure  rate and  prognosis   depends   mainly   on  the  early  detection   and  diagnosis  of   the  disease. At  the  moment, identification  of  blood  disorders  is  through   visual  inspection  of  microscopic  images  by  examining  changes  like  texture, geometry, colour  and   statistical  analysis  of  images . This  project  aims  to  preliminary  of  developing  a  detection  of  leukemia  types  using   microscopic  blood  sample using MATLAB. Images  are  used  as  they  are  cheap  and  do  not  expensive  for testing  and  lab  equipment.

Should Screening Programs Be Perfect?

PEDIATRICS ◽  
1983 ◽  
Vol 71 (2) ◽  
pp. 301-301
Author(s):  
PHILIP R. WYATT
Keyword(s):  
United States ◽  
Health Care ◽  
Early Detection ◽  
New England ◽  
Health Care System ◽  
The United States ◽  
Neonatal Hypothyroidism ◽  
Screening Programs ◽  
Care System

To the Editor.— The report of the New England Regional Screening Program1 on neonatal hypothyroidism is a stunning illustration of the vulnerability of screening programs. It is unfortunate that this experience will probably be used as an argument to minimize the input of screening programs in the health care system in the United States. The report illustrates that, in addition to the 2% of the screened population that eluded the program, 14 infants with hypothyroidism escaped the full benefits of early detection and treatment.

scholarly journals A Selective Ensemble Classification Method Combining Mammography Images with Ultrasound Images for Breast Cancer Diagnosis

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Jinyu Cong ◽  
Benzheng Wei ◽  
Yunlong He ◽  
Yilong Yin ◽  
Yuanjie Zheng
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Ensemble Classification ◽  
Classification Method ◽  
Ultrasound Images ◽  
Selective Ensemble ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

Breast cancer has been one of the main diseases that threatens women’s life. Early detection and diagnosis of breast cancer play an important role in reducing mortality of breast cancer. In this paper, we propose a selective ensemble method integrated with the KNN, SVM, and Naive Bayes to diagnose the breast cancer combining ultrasound images with mammography images. Our experimental results have shown that the selective classification method with an accuracy of 88.73% and sensitivity of 97.06% is efficient for breast cancer diagnosis. And indicator R presents a new way to choose the base classifier for ensemble learning.

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