3D CNN classification model for accurate diagnosis of coronavirus disease 2019 using computed tomography images

AbstractThe coronavirus disease (COVID-19) has been spreading rapidly around the world. As of August 25, 2020, 23.719 million people have been infected in many countries. The cumulative death toll exceeds 812,000. Early detection of COVID-19 is essential to provide patients with appropriate medical care and protect uninfected people. Leveraging a large computed tomography (CT) database from 1,112 patients provided by China Consortium of Chest CT Image Investigation (CC-CCII), we investigated multiple solutions in detecting COVID-19 and distinguished it from other common pneumonia (CP) and normal controls. We also compared the performance of different models for complete and segmented CT slices. In particular, we studied the effects of CT-superimposition depths into volumes on the performance of our models. The results show that the optimal model can identify the COVID-19 slices with 99.76% accuracy (99.96% recall, 99.35% precision and 99.65% F1-score). The overall performance for three-way classification obtained 99.24% accuracy and the area under the receiver operating characteristic curve (AUROC) of 0.9986. To the best of our knowledge, our method achieves the highest accuracy and recall with the largest public available COVID-19 CT dataset. Our model can help radiologists and physicians perform rapid diagnosis, especially when the healthcare system is overloaded.

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Improvement in the Convolutional Neural Network for Computed Tomography Images

Applied Sciences ◽

10.3390/app11041505 ◽

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.

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Pulmonary lesion subtypes recognition of COVID-19 from radiomics data with three-dimensional texture characterization in computed tomography images

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00961-w ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Wei Li ◽

Yangyong Cao ◽

Kun Yu ◽

Yibo Cai ◽

Feng Huang ◽

...

Keyword(s):

Computed Tomography ◽

Feature Selection ◽

Random Forest ◽

Data Augmentation ◽

Critical Role ◽

Three Dimensional ◽

Ground Glass Opacity ◽

Classification Model ◽

Volume Data ◽

Computed Tomography Images

Abstract Background The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT (computed tomography) examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are GGO (ground-glass opacity), cord, solid and subsolid. A computer-aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three-dimensional texture descriptors are applied on the volume data of lesions as well as shape and first-order features. The massive feature data are selected by HAFS (hybrid adaptive feature selection) algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (93.06%, 96.84%, 99.58%, and 94.30%), the recall is (95.52%, 91.58%, 95.80% and 80.75%) and the f-score is (93.84%, 92.37%, 95.47%, and 84.42%). Conclusion The three-dimensional radiomics features used in this paper can better express the high-level information of COVID-19 lesions in CT slices. HAFS method aggregates the results of multiple feature selection algorithms intersects with traditional methods to filter out redundant features more accurately. After selection, the subtype of COVID-19 lesion can be judged by inputting the features into the RF (random forest) model, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

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An experimental study on dynamic computed tomography images and time-density curves in acute renal failure

Journal of the Korean Radiological Society ◽

10.3348/jkrs.1987.23.6.887 ◽

1987 ◽

Vol 23 (6) ◽

pp. 887

Author(s):

D Y Kim ◽

H S Kang ◽

K M Yeoun ◽

C W Kim ◽

M C Han ◽

...

Keyword(s):

Computed Tomography ◽

Experimental Study ◽

Renal Failure ◽

Acute Renal Failure ◽

Dynamic Computed Tomography ◽

Computed Tomography Images ◽

Time Density

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An Optimization of System for Automatic Recognition of Ischemic Stroke Areas in Computed Tomography Images

Informatica ◽

10.15388/informatica.2007.196 ◽

2007 ◽

Vol 18 (4) ◽

pp. 603-614

Author(s):

Darius Grigaitis ◽

Vaida Bartkutė ◽

Leonidas Sakalauskas

Keyword(s):

Computed Tomography ◽

Ischemic Stroke ◽

Automatic Recognition ◽

Computed Tomography Images

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An Invasive Hemolymphangioma of the Pancreas in a Young Woman

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666190103110747 ◽

2019 ◽

Vol 21 (10) ◽

pp. 798-800 ◽

Cited By ~ 1

Author(s):

Zhijun Zhang ◽

Qinghong Ke ◽

Weiliang Xia ◽

Xiuming Zhang ◽

Yan Shen ◽

...

Keyword(s):

Computed Tomography ◽

Case Report ◽

Young Woman ◽

Benign Tumor ◽

Abdominal Distension ◽

Accurate Diagnosis ◽

Cystic Tumor ◽

Rare Benign Tumor ◽

Radical Surgical Resection

Background: Hemolymphangioma is a rare benign tumor. To the best of our knowledge, there were only 10 reports of this tumor of the pancreas until March 2018. Case Report: Here, we reported a large invasive hemolymphangioma of the pancreas in a young woman with a complaint of abdominal distension and an epigastric mass about 3 weeks. She was found to have a huge multilocular cystic tumor at the neck and body of pancreas on computed tomography. She was eventually diagnosed with hemolymphangioma of the pancreas after operation. After 2 years of follow-up, there was no signs of recurrence. Conclusion: From our case and literature, we can conclude that hemolymphangioma of the pancreas is uncommon benign tumor, and it is hard to make an accurate diagnosis preoperatively. Radical surgical resection should be performed whenever possible. The prognosis of this disease seems good.

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