scholarly journals Using Multi-level Convolutional Neural Network for Classification of Lung Nodules on CT images

2018 ◽  
Author(s):  
Juan Lyu ◽  
Sai Ho Ling
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Lung Nodules ◽  
Multi Level

scholarly journals Squeeze-and-Excitation Convolutional Neural Network for Classification of Malignant and Benign Lung Nodules

2021 ◽  
Vol 12 (2) ◽  
pp. 153-158
Author(s):  
Ying Chen ◽  
◽  
Weiwei Du ◽  
Xiaojie Duan ◽  
Yanhe Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Lung Nodules

Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features

2019 ◽  
Vol 10 (10) ◽  
pp. 4135-4149 ◽  
Author(s):  
Furqan Shaukat ◽  
Gulistan Raja ◽  
Rehan Ashraf ◽  
Shehzad Khalid ◽  
Mudassar Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Ct Images ◽  
Lung Nodules ◽  
Artificial Neural

scholarly journals Classification of malignancy of lung nodules in CT images using Convolutional Neural Network

2020 ◽  
Author(s):  
Giovanni Da Silva ◽  
Aristófanes Silva ◽  
Anselmo De Paiva ◽  
Marcelo Gattass
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Survival Rates ◽  
Texture Features ◽  
Image Database ◽  
Lung Nodules ◽  
Learning Technique

Lung cancer presents the highest mortality rate, besides being one of the smallest survival rates after diagnosis. Thereby, early detection is extremely important for the diagnosis and treatment. This paper proposes three different architectures of Convolutional Neural Network (CNN), which is a deep learning technique, for classification of malignancy of lung nodules without computing the morphology and texture features. The methodology was tested onto the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best accuracy of 82.3%, sensitivity of 79.4% and specificity 83.8%.

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.

scholarly journals Classification of Aortic Dissection and Rupture on Post-contrast CT Images Using a Convolutional Neural Network

2019 ◽  
Vol 32 (6) ◽  
pp. 939-946 ◽  
Author(s):  
Robert J. Harris ◽  
Shwan Kim ◽  
Jerry Lohr ◽  
Steve Towey ◽  
Zeljko Velichkovich ◽  
...  
Keyword(s):  
Neural Network ◽  
Aortic Dissection ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Post Contrast ◽  
Contrast Ct
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