Related references
Note: Only part of the references are listed.A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis
Chenggong Yan et al.
EUROPEAN RADIOLOGY (2022)
Deep-Learning-Aided Detection of Mycobacteria in Pathology Specimens Increases the Sensitivity in Early Diagnosis of Pulmonary Tuberculosis Compared with Bacteriology Tests
Yoshiaki Zaizen et al.
DIAGNOSTICS (2022)
Deep learning model for detection of COVID-19 utilizing the chest X-ray images
Shahanaz Abdul Gafoor et al.
COGENT ENGINEERING (2022)
Performance analysis of machine learning based optimized feature selection approaches for breast cancer diagnosis
Ajay Sharma et al.
International Journal of Information Technology (Singapore) (2021)
CO-IRv2: Optimized InceptionResNetV2 for COVID-19 detection from chest CT images
M. Rubaiyat Hossain Mondal et al.
PLOS ONE (2021)
Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A Review
M. Rubaiyat Hossain Mondal et al.
CURRENT MEDICAL IMAGING (2021)
Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network
Seung Seog Han et al.
JAMA DERMATOLOGY (2020)
TzanckNet: a convolutional neural network to identify cells in the cytology of erosive-vesiculobullous diseases
Mehmet Alican Noyan et al.
SCIENTIFIC REPORTS (2020)
Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability
Seelwan Sathitratanacheewin et al.
HELIYON (2020)
Automated TB classification using ensemble of deep architectures
Rahul Hooda et al.
MULTIMEDIA TOOLS AND APPLICATIONS (2019)
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
F. Pasa et al.
SCIENTIFIC REPORTS (2019)
Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data
Seok-Jae Heo et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2019)
Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs
Szilard Vajda et al.
JOURNAL OF MEDICAL SYSTEMS (2018)
Detecting drug-resistant tuberculosis in chest radiographs
Stefan Jaeger et al.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY (2018)
Comparing Deep Learning Models for Population Screening using Chest Radiography
R. Sivaramakrishnan et al.
MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS (2018)
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva et al.
NATURE (2017)
Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks
Paras Lakhani et al.
RADIOLOGY (2017)
Pre-trained convolutional neural networks as feature extractors for tuberculosis detection
U. K. Lopes et al.
COMPUTERS IN BIOLOGY AND MEDICINE (2017)
Automated methods for the summarization of electronic health records
Rimma Pivovarov et al.
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION (2015)
Role of Gist and PHOG Features in Computer-Aided Diagnosis of Tuberculosis without Segmentation
Arun Chauhan et al.
PLOS ONE (2014)
Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists' detection of pulmonary nodules
J Shiraishi et al.
AMERICAN JOURNAL OF ROENTGENOLOGY (2000)
Share Paper
