scholarly journals Survey on Recent Works in Computed Tomography based Computer ‑ Aided Diagnosis of Liver using Deep Learning Techniques

2020 ◽  
Vol 5 (7) ◽  
pp. 173-181
Author(s):  
E. Emerson Nithiyaraj ◽  
S. Arivazhagan
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Imaging Modality ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Machine Learning Algorithms ◽  
Learning Technology ◽  
X Rays ◽  
Computer Aided ◽  
Aided Diagnosis

Computed tomography (CT) scanning is a non-invasive diagnostic imaging technique that provides more detailed information about the liver than standard X-rays. Unlike ultrasound (US) examination, the quality of the CT image is not highly operator dependent. Plenty of works has been done using computer-aided diagnosis (CAD) for liver using conventional machine learning algorithms with better results. Recent advances especially in deep learning technology, can detect, classify, segment patterns in medical images where the advancements in deep learning has been shifted to medical domain also. One of the core abilities of deep learning is that they could learn feature representations automatically from data instead of feeding hand crafted features based on application. In this review, the basics of deep learning is introduced and their success in liver segmentation and lesion detection, classification using CT imaging modality is reviewed and their different network architectures is also discussed. Transfer learning is an interesting approach in deep learning which is also discussed. So, deep learning and CAD system has made a huge impact and has produced enhanced performance in healthcare industry.

scholarly journals A Deep Learning-based Computer-aided Diagnosis System for Mammographic Lesion Detection

2018 ◽  
Vol 54 (8) ◽  
pp. 659-669 ◽  
Author(s):  
Shintaro SUZUKI ◽  
Xiaoyong ZHANG ◽  
Noriyasu HOMMA ◽  
Kei ICHIJI ◽  
Yumi TAKANE ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Lesion Detection ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology

2017 ◽  
Vol 25 (5) ◽  
pp. 751-763 ◽  
Author(s):  
Yuchen Qiu ◽  
Shiju Yan ◽  
Rohith Reddy Gundreddy ◽  
Yunzhi Wang ◽  
Samuel Cheng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Mass ◽  
Learning Technology ◽  
New Approach ◽  
Computer Aided ◽  
Mass Classification ◽  
Aided Diagnosis

scholarly journals Pneumoconiosis computer aided diagnosis system based on X-rays and deep learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Fan Yang ◽  
Zhi-Ri Tang ◽  
Jing Chen ◽  
Min Tang ◽  
Shengchun Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Classification Model ◽  
Lung Region ◽  
X Rays ◽  
Diagnosis System ◽  
Normal People ◽  
Computer Aided ◽  
Aided Diagnosis

Abstract Purpose The objective of this study is to construct a computer aided diagnosis system for normal people and pneumoconiosis using X-raysand deep learning algorithms. Materials and methods 1760 anonymous digital X-ray images of real patients between January 2017 and June 2020 were collected for this experiment. In order to concentrate the feature extraction ability of the model more on the lung region and restrain the influence of external background factors, a two-stage pipeline from coarse to fine was established. First, the U-Net model was used to extract the lung regions on each sides of the collection images. Second, the ResNet-34 model with transfer learning strategy was implemented to learn the image features extracted in the lung region to achieve accurate classification of pneumoconiosis patients and normal people. Results Among the 1760 cases collected, the accuracy and the area under curve of the classification model were 92.46% and 89% respectively. Conclusion The successful application of deep learning in the diagnosis of pneumoconiosis further demonstrates the potential of medical artificial intelligence and proves the effectiveness of our proposed algorithm. However, when we further classified pneumoconiosis patients and normal subjects into four categories, we found that the overall accuracy decreased to 70.1%. We will use the CT modality in future studies to provide more details of lung regions.

scholarly journals Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

2019 ◽  
Vol 5 (1) ◽  
pp. 223-226
Author(s):  
Max-Heinrich Laves ◽  
Sontje Ihler ◽  
Tobias Ortmaier ◽  
Lüder A. Kahrs
Keyword(s):  
Monte Carlo ◽  
Patient Safety ◽  
Deep Learning ◽  
Epistemic Uncertainty ◽  
Computer Aided Diagnosis ◽  
Uncertainty Estimation ◽  
Test Time ◽  
Prediction Uncertainty ◽  
Computer Aided ◽  
Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

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