Classification of Lung Nodule Malignancy Risk on Computed Tomography Images Using Convolutional Neural Network: A Comparison Between 2D and 3D Strategies

2017 ◽  
pp. 91-101 ◽  
Author(s):  
Xingjian Yan ◽  
Jianing Pang ◽  
Hang Qi ◽  
Yixin Zhu ◽  
Chunxue Bai ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Lung Nodule ◽  
Malignancy Risk ◽  
Computed Tomography Images ◽  
2D And 3D

scholarly journals Improvement in the Convolutional Neural Network for Computed Tomography Images

2021 ◽  
Vol 11 (4) ◽  
pp. 1505
Author(s):  
Keisuke Manabe ◽  
Yusuke Asami ◽  
Tomonari Yamada ◽  
Hiroyuki Sugimori
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Classification Model ◽  
Max Pooling ◽  
Computed Tomography Images ◽  
Input Size ◽  
Filter Size

Background and purpose. This study evaluated a modified specialized convolutional neural network (CNN) to improve the accuracy of medical images. Materials and Methods. We defined computed tomography (CT) images as belonging to one of the following 10 classes: head, neck, chest, abdomen, and pelvis with and without contrast media, with 10,000 images per class. We modified the CNN based on the AlexNet with an input size of 512 × 512. We resized the filter sizes of the convolution layer and max pooling. Using these modified CNNs, various models were created and evaluated. The improved CNN was evaluated to classify the presence or absence of the pancreas in the CT images. We compared the overall accuracy, which was calculated from images not used for training, to that of the ResNet. Results. The overall accuracies of the most improved CNN and ResNet in the 10 classes were 94.8% and 89.3%, respectively. The filter sizes of the improved CNN for the convolution layer were (13, 13), (7, 7), (5, 5), (5, 5), and (5, 5) in order from the first layer, and that of max-pooling was (7, 7). The calculation times of the most improved CNN and ResNet were 56 and 120 min, respectively. Regarding the classification of the pancreas, the overall accuracies of the most improved CNN and ResNet were 75.75% and 58.25%, respectively. The calculation times of the most improved CNN and ResNet were 36 and 55 min, respectively. Conclusion. By optimizing the filter size of the convolution layer and max-pooling of 512 × 512 images, we quickly obtained a highly accurate medical image classification model. This improved CNN can be useful for classifying lesions and anatomies for related diagnostic aid applications.

Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

2015 ◽  
Vol 26 (1) ◽  
pp. 195-202 ◽  
Author(s):  
Francesco Ciompi ◽  
Bartjan de Hoop ◽  
Sarah J. van Riel ◽  
Kaman Chung ◽  
Ernst Th. Scholten ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Convolutional Neural Network ◽  
Automatic Classification

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

2020 ◽  
Vol 134 (4) ◽  
pp. 328-331 ◽  
Author(s):  
P Parmar ◽  
A-R Habib ◽  
D Mendis ◽  
A Daniel ◽  
M Duvnjak ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computed Tomography ◽  
Diagnostic Accuracy ◽  
Convolutional Neural Network ◽  
Middle Turbinate ◽  
Concha Bullosa ◽  
Computed Tomography Images ◽  
Computed Tomography Scans ◽  
Neural Network Algorithm

AbstractObjectiveConvolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.MethodConsecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.ResultsThe trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.ConclusionA trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

scholarly journals Special Convolutional Neural Network for Identification and Positioning of Interstitial Lung Disease Patterns in Computed Tomography Images

2021 ◽  
Vol 31 (4) ◽  
pp. 730-738
Author(s):  
Sunita Agarwala ◽  
Abhishek Kumar ◽  
Ashis Kumar Dhara ◽  
Sumitra Basu Thakur ◽  
Anup Sadhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Interstitial Lung Disease ◽  
Convolutional Neural Network ◽  
Lung Disease ◽  
Disease Patterns ◽  
Computed Tomography Images
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