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 Deep Learning-Based Computer-Aided Diagnosis System for Gastroscopy Image Classification Using Synthetic Data

2021 ◽  
Vol 11 (2) ◽  
pp. 760
Author(s):  
Yun-ji Kim ◽  
Hyun Chin Cho ◽  
Hyun-chong Cho
Keyword(s):  
Deep Learning ◽  
Disease Diagnosis ◽  
Computer Aided Diagnosis ◽  
Medical Data ◽  
Classification Model ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Original Training ◽  
Computer Aided ◽  
Aided Diagnosis

Gastric cancer has a high mortality rate worldwide, but it can be prevented with early detection through regular gastroscopy. Herein, we propose a deep learning-based computer-aided diagnosis (CADx) system applying data augmentation to help doctors classify gastroscopy images as normal or abnormal. To improve the performance of deep learning, a large amount of training data are required. However, the collection of medical data, owing to their nature, is highly expensive and time consuming. Therefore, data were generated through deep convolutional generative adversarial networks (DCGAN), and 25 augmentation policies optimized for the CIFAR-10 dataset were implemented through AutoAugment to augment the data. Accordingly, a gastroscopy image was augmented, only high-quality images were selected through an image quality-measurement method, and gastroscopy images were classified as normal or abnormal through the Xception network. We compared the performances of the original training dataset, which did not improve, the dataset generated through the DCGAN, the dataset augmented through the augmentation policies of CIFAR-10, and the dataset combining the two methods. The dataset combining the two methods delivered the best performance in terms of accuracy (0.851) and achieved an improvement of 0.06 over the original training dataset. We confirmed that augmenting data through the DCGAN and CIFAR-10 augmentation policies is most suitable for the classification model for normal and abnormal gastric endoscopy images. The proposed method not only solves the medical-data problem but also improves the accuracy of gastric disease diagnosis.

Computer-aided diagnosis system for breast ultrasound images using deep learning

2019 ◽  
Vol 64 (23) ◽  
pp. 235013 ◽  
Author(s):  
Hiroki Tanaka ◽  
Shih-Wei Chiu ◽  
Takanori Watanabe ◽  
Setsuko Kaoku ◽  
Takuhiro Yamaguchi
Keyword(s):  
Deep Learning ◽  
Computer Aided Diagnosis ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

Evaluation of a deep learning‐based computer‐aided diagnosis system for distinguishing benign from malignant thyroid nodules in ultrasound images

2020 ◽  
Vol 47 (9) ◽  
pp. 3952-3960 ◽  
Author(s):  
Chao Sun ◽  
Yukang Zhang ◽  
Qing Chang ◽  
Tianjiao Liu ◽  
Shaohang Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Thyroid Nodules ◽  
Computer Aided Diagnosis ◽  
Ultrasound Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

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 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.

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