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A survey of computer-aided diagnosis of lung nodules from CT scans using deep learning
Y Gu, J Chi, J Liu, L Yang, B Zhang, D Yu… - Computers in biology …, 2021 - Elsevier
Lung cancer has one of the highest mortalities of all cancers. According to the National Lung
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Screening Trial, patients who underwent low-dose computed tomography (CT) scanning …
Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications
Artificial intelligence (AI) has been present in some guise within the field of radiology for over
50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date …
50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date …
Deep neural network correlation learning mechanism for CT brain tumor detection
Modern medical clinics support medical examinations with computer systems which use
Computational Intelligence on the way to detect potential health problems in more efficient …
Computational Intelligence on the way to detect potential health problems in more efficient …
Deep-learning framework to detect lung abnormality–A study with chest X-Ray and lung CT scan images
Lung abnormalities are highly risky conditions in humans. The early diagnosis of lung
abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This …
abnormalities is essential to reduce the risk by enabling quick and efficient treatment. This …
Development and validation of a deep learning model for non–small cell lung cancer survival
Importance There is a lack of studies exploring the performance of a deep learning survival
neural network in non–small cell lung cancer (NSCLC). Objectives To compare the …
neural network in non–small cell lung cancer (NSCLC). Objectives To compare the …
[HTML][HTML] Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review
The aim of this study was to provide an overview of the literature available on machine
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
learning (ML) algorithms applied to the Lung Image Database Consortium Image Collection …
Convolutional neural networks-based lung nodule classification: A surrogate-assisted evolutionary algorithm for hyperparameter optimization
This article investigates deep neural networks (DNNs)-based lung nodule classification with
hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally …
hyperparameter optimization. Hyperparameter optimization in DNNs is a computationally …
Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis
LT Thong, HS Chou, HSJ Chew, LAU Ying - Lung Cancer, 2023 - Elsevier
Background Lung cancer is the principal cause of cancer-related deaths worldwide. Early
detection of lung cancer with screening is indispensable to reduce the high morbidity and …
detection of lung cancer with screening is indispensable to reduce the high morbidity and …
[HTML][HTML] A radiomics-based decision support tool improves lung cancer diagnosis in combination with the Herder score in large lung nodules
Background Large lung nodules (≥ 15 mm) have the highest risk of malignancy, and may
exhibit important differences in phenotypic or clinical characteristics to their smaller …
exhibit important differences in phenotypic or clinical characteristics to their smaller …
Classification of non-small cell lung cancer using one-dimensional convolutional neural network
Abstract Non-Small Cell Lung Cancer (NSCLC) is a major lung cancer type. Proper
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …
diagnosis depends mainly on tumor staging and grading. Pathological prognosis often faces …