Relative location prediction in CT scan images using convolutional neural networks

Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

doi:10.1016/j.cmpb.2018.03.025

Cited by 8 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the other hand, there so many researches utilized Convolutional Neural Network (CNN)-based algorithm have shown their own superiority in CT scans image processing. Some of them are shown by [7][8][9][10]. In the opposite, some researches in SVMS also shows the same, as explored by [11], [12] and [13].…”

Section: Introductionmentioning

confidence: 70%

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Sentana¹,

Asri²,

Jawas³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

View full text Add to dashboard Cite

Convolutional Neural Networks (CNN) are natural based classification algorithm that combine Multiple Layer Perceptron (MLPs). Meanwhile, support vector machines (SVM) is a mathematical-based classification algorithm that naturally have supervised learning models. In some research related to image processing, each algorithm has its owned supremacy as well as the drawback. None of the previous studies compare both algorithm when they are utilized to detect nodule located in the pulmonary or lung images produced by Computed Tomography (CT) scan. Hence, this research comparing the two algorithms in case of lung nodule detection in CT images, since detecting lung nodule in CT images is still challenging. SVM-based classifier is preceded by feature extraction as its common behavior of mathematical based classifier. There are three algorithms use to conduct feature extraction process, namely Hu moment invariant, Haralick and Color Histogram extraction. In the opposite, CNN-based classifier consists of three layers convolution for training and testing steps. The result shows that SVM has better results than CNN in case of computing speed. Meanwhile have a better accuracy in detecting lung nodule. The results of the test analysis show that the extractor feature when preprocessing conduct before being classified by SVM makes the computing process faster. The accuracy of SVM-based classifier can be improved by adjusting some computation variables in feature extraction stages, such as adding more bins in the color histogram extraction. Those adjustment will lead to more computation times.

show abstract

Section: Introductionmentioning

confidence: 70%

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Sentana¹,

Asri²,

Jawas³

et al. 2018

Proceedings of the International Conference on Science and Technology (ICST 2018)

View full text Add to dashboard Cite

show abstract

“…MAE can also perform better than MSE in related HYC prediction problems. [ 12 ] As a result, our study selected MAE as the loss function to predict brain age.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning

Duan

Zhang

Zhang³

et al. 2020

Medicine

View full text Add to dashboard Cite

To design and develop artificial intelligence (AI) hydrocephalus (HYC) imaging diagnostic model using a transfer learning algorithm and evaluate its application in the diagnosis of HYC by non-contrast material-enhanced head computed tomographic (CT) images. A training and validation dataset of non-contrast material-enhanced head CT examinations that comprised of 1000 patients with HYC and 1000 normal people with no HYC accumulating to 28,500 images. Images were pre-processed, and the feature variables were labeled. The feature variables were extracted by the neural network for transfer learning. AI algorithm performance was tested on a separate dataset containing 250 examinations of HYC and 250 of normal. Resident, attending and consultant in the department of radiology were also tested with the test sets, their results were compared with the AI model. Final model performance for HYC showed 93.6% sensitivity (95% confidence interval: 77%, 97%) and 94.4% specificity (95% confidence interval: 79%, 98%), with area under the characteristic curve of 0.93. Accuracy rate of model, resident, attending, and consultant were 94.0%, 93.4%, 95.6%, and 97.0%. AI can effectively identify the characteristics of HYC from CT images of the brain and automatically analyze the images. In the future, AI can provide auxiliary diagnosis of image results and reduce the burden on junior doctors.

show abstract

“…8, the reconstruction accuracy of the indoor scenario CSI decreases a lot but there is little accuracy reduction of the outdoor scenario CSI reconstruction. During training, the loss of the outdoor CSI is much larger than that of the indoor CSI and the MSE loss function primarily smoothes the outdoor CSI reconstruction error, ignoring the indoor CSI [41].…”

Section: ) Neural Network Capacitymentioning

confidence: 99%

Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

Guo

Wen

Jin

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

280

178

View full text Add to dashboard Cite

Massive multiple-input multiple-output (MIMO) is a promising technology to increase link capacity and energy efficiency. However, these benefits are based on available channel state information (CSI) at the base station (BS). Therefore, user equipment (UE) needs to keep on feeding CSI back to the BS, thereby consuming precious bandwidth resource. Large-scale antennas at the BS for massive MIMO seriously increase this overhead. In this paper, we propose a multiple-rate compressive sensing neural network framework to compress and quantize the CSI. This framework not only improves reconstruction accuracy but also decreases storage space at the UE, thus enhancing the system feasibility. Specifically, we establish two network design principles for CSI feedback, propose a new network architecture, CsiNet+, according to these principles, and develop a novel quantization framework and training strategy.Next, we further introduce two different variable-rate approaches, namely, SM-CsiNet+ and PM-CsiNet+, which decrease the parameter number at the UE by 38.0% and 46.7%, respectively. Experimental results show that CsiNet+ outperforms the state-of-the-art network by a margin but only slightly increases the parameter number. We also investigate the compression and reconstruction mechanism behind deep learning-based CSI feedback methods via parameter visualization, which provides a guideline for subsequent research.

show abstract

Relative location prediction in CT scan images using convolutional neural networks

Cited by 8 publications

References 16 publications

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

CNN and SVM Based Classifier Comparation to Detect Lung Nodule In Computed Tomography Images

Evaluation of an artificial intelligent hydrocephalus diagnosis model based on transfer learning

Convolutional Neural Network-Based Multiple-Rate Compressive Sensing for Massive MIMO CSI Feedback: Design, Simulation, and Analysis

Contact Info

Product

Resources

About