Tumor detection and classification in breast mammography based on fine-tuned convolutional neural networks

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Abeer Ahmed ◽  
Arabi Keshk ◽  
Osama M. Abo-Seida ◽  
Mohamed Sakr
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Tumor Detection

Big data analysis for brain tumor detection: Deep convolutional neural networks

2018 ◽  
Vol 87 ◽  
pp. 290-297 ◽  
Author(s):  
Javeria Amin ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Steven Lawrence Fernandes
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Brain Tumor ◽  
Data Analysis ◽  
Convolutional Neural Networks ◽  
Tumor Detection ◽  
Big Data Analysis ◽  
Deep Convolutional Neural Networks

Deriving tumor detection models using convolutional neural networks from MRI of human brain scans

2020 ◽  
Vol 12 (2) ◽  
pp. 403-408
Author(s):  
T. Kalaiselvi ◽  
S. T. Padmapriya ◽  
P. Sriramakrishnan ◽  
Karuppanagounder Somasundaram
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Convolutional Neural Networks ◽  
Tumor Detection ◽  
Brain Scans

Development of automatic glioma brain tumor detection system using deep convolutional neural networks

2020 ◽  
Vol 30 (4) ◽  
pp. 926-938 ◽  
Author(s):  
Thiruvenkadam Kalaiselvi ◽  
Thiyagarajan Padmapriya ◽  
Padmanaban Sriramakrishnan ◽  
Venugopal Priyadharshini
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Detection System ◽  
Tumor Detection ◽  
Deep Convolutional Neural Networks

scholarly journals Convolutional Neural Networks for Classifying Laterality of Vestibular Schwannomas on Single MRI Slices—A Feasibility Study

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1676
Author(s):  
Philipp Sager ◽  
Lukas Näf ◽  
Erwin Vu ◽  
Tim Fischer ◽  
Paul M. Putora ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
External Validation ◽  
Tumor Detection ◽  
Tumor Location ◽  
Vestibular Schwannomas ◽  
Internal Validation ◽  
Contrast Enhanced ◽  
The Difference ◽  
Validation Set

Introduction: Many proposed algorithms for tumor detection rely on 2.5/3D convolutional neural networks (CNNs) and the input of segmentations for training. The purpose of this study is therefore to assess the performance of tumor detection on single MRI slices containing vestibular schwannomas (VS) as a computationally inexpensive alternative that does not require the creation of segmentations. Methods: A total of 2992 T1-weighted contrast-enhanced axial slices containing VS from the MRIs of 633 patients were labeled according to tumor location, of which 2538 slices from 539 patients were used for training a CNN (ResNet-34) to classify them according to the side of the tumor as a surrogate for detection and 454 slices from 94 patients were used for internal validation. The model was then externally validated on contrast-enhanced and non-contrast-enhanced slices from a different institution. Categorical accuracy was noted, and the results of the predictions for the validation set are provided with confusion matrices. Results: The model achieved an accuracy of 0.928 (95% CI: 0.869–0.987) on contrast-enhanced slices and 0.795 (95% CI: 0.702–0.888) on non-contrast-enhanced slices from the external validation cohorts. The implementation of Gradient-weighted Class Activation Mapping (Grad-CAM) revealed that the focus of the model was not limited to the contrast-enhancing tumor but to a larger area of the cerebellum and the cerebellopontine angle. Conclusions: Single-slice predictions might constitute a computationally inexpensive alternative to training 2.5/3D-CNNs for certain detection tasks in medical imaging even without the use of segmentations. Head-to-head comparisons between 2D and more sophisticated architectures could help to determine the difference in accuracy, especially for more difficult tasks.

scholarly journals Symptomatically Brain Tumor Detection Using Convolutional Neural Networks

2021 ◽  
Vol 1022 ◽  
pp. 012078
Author(s):  
Varun Totakura ◽  
E. Madhusudhana Reddy ◽  
Bhargava Reddy Vuribindi
Keyword(s):  
Neural Networks ◽  
Brain Tumor ◽  
Convolutional Neural Networks ◽  
Tumor Detection

scholarly journals Automated liver tumor detection in abdominal ultrasonography with a modified faster region-based convolutional neural networks (Faster R-CNN) architecture

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Kenji Karako ◽  
Yuichiro Mihara ◽  
Junichi Arita ◽  
Akihiko Ichida ◽  
Sung Kwan Bae ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Liver Tumor ◽  
Tumor Detection ◽  
Abdominal Ultrasonography
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