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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Health Sciences / Faculty of Health Sciences >
Peer-reviewed Journal Articles, etc >
Improvement in the Convolutional Neural Network for Computed Tomography Images
| Title: | Improvement in the Convolutional Neural Network for Computed Tomography Images |
| Authors: | Manabe, Keisuke Browse this author | | Asami, Yusuke Browse this author | | Yamada, Tomonari Browse this author | | Sugimori, Hiroyuki Browse this author →KAKEN DB |
| Keywords: | deep learning | | convolutional neural network | | image classification |
| Issue Date: | Feb-2021 |
| Publisher: | MDPI |
| Journal Title: | Applied sciences |
| Volume: | 11 |
| Issue: | 4 |
| Start Page: | 1505 |
| Publisher DOI: | 10.3390/app11041505 |
| Abstract: | 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 x 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 x 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. |
| Type: | article |
| URI: | http://hdl.handle.net/2115/81141 |
| Appears in Collections: | 保健科学院・保健科学研究院 (Graduate School of Health Sciences / Faculty of Health Sciences) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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