A Novel Deep Learning Based Computer-Aided Diagnosis System Improves the Accuracy and Efficiency of Radiologists in Reading Biparametric Magnetic Resonance Images of the Prostate

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
David J. Winkel ◽  
Angela Tong ◽  
Bin Lou ◽  
Ali Kamen ◽  
Dorin Comaniciu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Magnetic Resonance ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Diagnosis System ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals PBTNet: A New Computer-Aided Diagnosis System for Detecting Primary Brain Tumors

2021 ◽  
Vol 9 ◽  
Author(s):  
Si-Yuan Lu ◽  
Suresh Chandra Satapathy ◽  
Shui-Hua Wang ◽  
Yu-Dong Zhang
Keyword(s):  
Magnetic Resonance ◽  
Brain Tumors ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Functional Link ◽  
Diagnosis System ◽  
Primary Brain Tumors ◽  
Computer Aided ◽  
Aided Diagnosis

Brain tumors are among the leading human killers. There are over 120 different types of brain tumors, but they mainly fall into two groups: primary brain tumors and metastatic brain tumors. Primary brain tumors develop from normal brain cells. Early and accurate detection of primary brain tumors is vital for the treatment of this disease. Magnetic resonance imaging is the most common method to diagnose brain diseases, but the manual interpretation of the images suffers from high inter-observer variance. In this paper, we presented a new computer-aided diagnosis system named PBTNet for detecting primary brain tumors in magnetic resonance images. A pre-trained ResNet-18 was selected as the backbone model in our PBTNet, but it was fine-tuned only for feature extraction. Then, three randomized neural networks, Schmidt neural network, random vector functional-link, and extreme learning machine served as the classifiers in the PBTNet, which were trained with the features and their labels. The final predictions of the PBTNet were generated by the ensemble of the outputs from the three classifiers. 5-fold cross-validation was employed to evaluate the classification performance of the PBTNet, and experimental results demonstrated that the proposed PBTNet was an effective tool for the diagnosis of primary brain tumors.

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

scholarly journals Computer-Aided Diagnosis Systems for Brain Diseases in Magnetic Resonance Images

Algorithms ◽  
2009 ◽  
Vol 2 (3) ◽  
pp. 925-952 ◽  
Author(s):  
Hidetaka Arimura ◽  
Taiki Magome ◽  
Yasuo Yamashita ◽  
Daisuke Yamamoto
Keyword(s):  
Magnetic Resonance ◽  
Computer Aided Diagnosis ◽  
Magnetic Resonance Images ◽  
Brain Diseases ◽  
Computer Aided ◽  
Aided Diagnosis
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