Breast MRI lesion classification: Improved performance of human readers with a backpropagation neural network computer-aided diagnosis (CAD) system

2007 ◽  
Vol 25 (1) ◽  
pp. 89-95 ◽  
Author(s):  
Lina Arbash Meinel ◽  
Alan H. Stolpen ◽  
Kevin S. Berbaum ◽  
Laurie L. Fajardo ◽  
Joseph M. Reinhardt
Keyword(s):  
Neural Network ◽  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Backpropagation Neural Network ◽  
Cad System ◽  
Network Computer ◽  
Computer Aided ◽  
Improved Performance ◽  
Aided Diagnosis ◽  
Lesion Classification

Neural network vector quantization improves the diagnostic quality of computer-aided diagnosis in dynamic breast MRI

2007 ◽  
Author(s):  
Axel Wismüller ◽  
Anke Meyer-Baese ◽  
Gerda L. Leinsinger ◽  
Oliver Lange ◽  
Thomas Schlossbauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Breast Mri ◽  
Computer Aided Diagnosis ◽  
Diagnostic Quality ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals COVID-19 Chest Computed Tomography to Stratify Severity and Disease Extension by Artificial Neural Network Computer-Aided Diagnosis

2020 ◽  
Vol 7 ◽  
Author(s):  
Alysson Roncally S. Carvalho ◽  
Alan Guimarães ◽  
Gabriel Madeira Werberich ◽  
Stephane Nery de Castro ◽  
Joana Sofia F. Pinto ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Roc Curve ◽  
Pulmonary Involvement ◽  
Computer Aided Diagnosis ◽  
Network Computer ◽  
Computer Aided ◽  
Artificial Neural ◽  
Kappa Agreement ◽  
Aided Diagnosis

Purpose: This work aims to develop a computer-aided diagnosis (CAD) to quantify the extent of pulmonary involvement (PI) in COVID-19 as well as the radiological patterns referred to as lung opacities in chest computer tomography (CT).Methods: One hundred thirty subjects with COVID-19 pneumonia who underwent chest CT at hospital admission were retrospectively studied (141 sets of CT scan images). Eighty-eight healthy individuals without radiological evidence of acute lung disease served as controls. Two radiologists selected up to four regions of interest (ROI) per patient (totaling 1,475 ROIs) visually regarded as well-aerated regions (472), ground-glass opacity (GGO, 413), crazy paving and linear opacities (CP/LO, 340), and consolidation (250). After balancing with 250 ROIs for each class, the density quantiles (2.5, 25, 50, 75, and 97.5%) of 1,000 ROIs were used to train (700), validate (150), and test (150 ROIs) an artificial neural network (ANN) classifier (60 neurons in a single-hidden-layer architecture). Pulmonary involvement was defined as the sum of GGO, CP/LO, and consolidation volumes divided by total lung volume (TLV), and the cutoff of normality between controls and COVID-19 patients was determined with a receiver operator characteristic (ROC) curve. The severity of pulmonary involvement in COVID-19 patients was also assessed by calculating Z scores relative to the average volume of parenchymal opacities in controls. Thus, COVID-19 cases were classified as mild (<cutoff of normality), moderate (cutoff of normality ≤ pulmonary involvement < Z score 3), and severe pulmonary involvement (Z score ≥3).Results: Cohen's kappa agreement between CAD and radiologist classification was 81% (79–84%, 95% CI). The ROC curve of PI by the ANN presented a threshold of 21.5%, sensitivity of 0.80, specificity of 0.86, AUC of 0.90, accuracy of 0.82, F score of 0.85, and 0.65 Matthews' correlation coefficient. Accordingly, 77 patients were classified as having severe pulmonary involvement reaching 55 ± 13% of the TLV (Z score related to controls ≥3) and presented significantly higher lung weight, serum C-reactive protein concentration, proportion of hospitalization in intensive care units, instances of mechanical ventilation, and case fatality.Conclusion: The proposed CAD aided in detecting and quantifying the extent of pulmonary involvement, helping to phenotype patients with COVID-19 pneumonia.

scholarly journals Computer-Aided Diagnosis Improves the Detection of Clinically Significant Prostate Cancer on Multiparametric-MRI: A Multi-Observer Performance Study Involving Inexperienced Readers

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 973
Author(s):  
Valentina Giannini ◽  
Simone Mazzetti ◽  
Giovanni Cappello ◽  
Valeria Maria Doronzio ◽  
Lorenzo Vassallo ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Computer Aided Diagnosis ◽  
Multiparametric Mri ◽  
Performance Study ◽  
Predictive Values ◽  
Cad System ◽  
Computer Aided ◽  
Clinically Significant ◽  
Sensitivity Specificity ◽  
Aided Diagnosis

Recently, Computer Aided Diagnosis (CAD) systems have been proposed to help radiologists in detecting and characterizing Prostate Cancer (PCa). However, few studies evaluated the performances of these systems in a clinical setting, especially when used by non-experienced readers. The main aim of this study is to assess the diagnostic performance of non-experienced readers when reporting assisted by the likelihood map generated by a CAD system, and to compare the results with the unassisted interpretation. Three resident radiologists were asked to review multiparametric-MRI of patients with and without PCa, both unassisted and assisted by a CAD system. In both reading sessions, residents recorded all positive cases, and sensitivity, specificity, negative and positive predictive values were computed and compared. The dataset comprised 90 patients (45 with at least one clinically significant biopsy-confirmed PCa). Sensitivity significantly increased in the CAD assisted mode for patients with at least one clinically significant lesion (GS > 6) (68.7% vs. 78.1%, p = 0.018). Overall specificity was not statistically different between unassisted and assisted sessions (94.8% vs. 89.6, p = 0.072). The use of the CAD system significantly increases the per-patient sensitivity of inexperienced readers in the detection of clinically significant PCa, without negatively affecting specificity, while significantly reducing overall reporting time.

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

Research on Computer-Aided Diagnosis of Alzheimer’s Disease Based on Heterogeneous Medical Data Fusion

2019 ◽  
Vol 33 (05) ◽  
pp. 1957001
Author(s):  
Yin Dai ◽  
Daoyun Qiu ◽  
Yang Wang ◽  
Sizhe Dong ◽  
Hong-Li Wang
Keyword(s):  
Neural Network ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Convolutional Neural Network ◽  
Effective Means ◽  
Computer Aided Diagnosis ◽  
Test Results ◽  
Back Propagation Algorithm ◽  
Computer Aided ◽  
Aided Diagnosis

Alzheimer’s disease is the third most expensive disease, only after cancer and cardiopathy. It is also the fourth leading cause of death in the elderly after cardiopathy, cancer, and cerebral palsy. The disease lacks specific diagnostic criteria. At present, there is still no definitive and effective means for preclinical diagnosis and treatment. It is the only disease that cannot be prevented and cured among the world’s top ten fatal diseases. It has now been proposed as a global issue. Computer-aided diagnosis of Alzheimer’s disease (AD) is mostly based on images at this stage. This project uses multi-modality imaging MRI/PET combining with clinical scales and uses deep learning-based computer-aided diagnosis to treat AD, improves the comprehensiveness and accuracy of diagnosis. The project uses Bayesian model and convolutional neural network to train experimental data. The experiment uses the improved existing network model, LeNet-5, to design and build a 10-layer convolutional neural network. The network uses a back-propagation algorithm based on a gradient descent strategy to achieve good diagnostic results. Through the calculation of sensitivity, specificity and accuracy, the test results were evaluated, good test results were obtained.

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