scholarly journals Classification of Pneumonia Cell Images Using Improved ResNet50 Model

2021 ◽  
Vol 38 (1) ◽  
pp. 165-173
Author(s):  
Ahmet Çınar ◽  
Muhammed Yıldırım ◽  
Yeşim Eroğlu
Keyword(s):  
Error Rates ◽  
Lung Imaging ◽  
Imaging Method ◽  
Data Sets ◽  
Accuracy Rate ◽  
Traditional Methods ◽  
X Ray ◽  
Qualified Personnel ◽  
Patient Will

Pneumonia is a disease caused by inflammation of the lung tissue that is transmitted by various means, primarily bacteria. Early and accurate diagnosis is important in reducing the morbidity and mortality of the disease. The primary imaging method used for the diagnosis of pneumonia is lung x-ray. While typical imaging findings of pneumonia may be present on lung imaging, nonspecific images may be present. In addition, many health units may not have qualified personnel to perform this procedure or there may be errors in diagnoses made by traditional methods. For this reason, computer systems can be used to prevent error rates that may occur in traditional methods. Many methods have been developed to train data sets. In this article, a new model has been developed based on the layers of the ResNet50. The developed model was compared with the architectures InceptionV3, AlexNet, GoogleNet, ResNet50 and DenseNet201. In the developed model, the maximum accuracy rate was achieved as 97.22%. The model developed was followed by DenseNet201, ResNet50, InceptionV3, GoogleNet and AlexNet, respectively, according to their accuracy. With these developed models, the diagnosis of pneumonia can be made early and accurately, and the treatment management of the patient will be determined quickly.

scholarly journals A convolutional neural network-based screening tool for X-ray serial crystallography

2018 ◽  
Vol 25 (3) ◽  
pp. 655-670 ◽  
Author(s):  
Tsung-Wei Ke ◽  
Aaron S. Brewster ◽  
Stella X. Yu ◽  
Daniela Ushizima ◽  
Chao Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Large Data ◽  
Large Data Sets ◽  
Training Data ◽  
Data Sets ◽  
X Ray ◽  
X Ray Crystallography ◽  
Automatic Image Processing

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization.

Global Inhomogeneity Index Evaluation of a DCT-based EIT Lung Imaging

2020 ◽  
Vol 6 (3) ◽  
pp. 36-39
Author(s):  
Rongqing Chen ◽  
Knut Möller
Keyword(s):  
Classical Method ◽  
Lung Imaging ◽  
Imaging Method ◽  
Reconstruction Method ◽  
Essential Information ◽  
Preliminary Results ◽  
Reconstruction Methods ◽  
Comparison Results ◽  
Index Evaluation ◽  
Inhomogeneity Index

AbstractPurpose: To evaluate a novel structural-functional DCT-based EIT lung imaging method against the classical EIT reconstruction. Method: Taken retrospectively from a former study, EIT data was evaluated using both reconstruction methods. For different phases of ventilation, EIT images are analyzed with respect to the global inhomogeneity (GI) index for comparison. Results: A significant less variant GI index was observed in the DCTbased method, compared to the index from classical method. Conclusion: The DCT-based method generates more accurate lung contour yet decreasing the essential information in the image which affects the GI index. These preliminary results must be consolidated with more patient data in different breathing states.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals Coherence tomography with broad bandwidth extreme ultraviolet and soft X-ray radiation

2021 ◽  
Vol 127 (4) ◽  
Author(s):  
S. Skruszewicz ◽  
S. Fuchs ◽  
J. J. Abel ◽  
J. Nathanael ◽  
J. Reinhard ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Near Infrared ◽  
Extreme Ultraviolet ◽  
Infrared Light ◽  
Imaging Method ◽  
Optical Coherence ◽  
Axial Resolution ◽  
Cross Sectional ◽  
X Ray ◽  
Broad Bandwidth

AbstractWe present an overview of recent results on optical coherence tomography with the use of extreme ultraviolet and soft X-ray radiation (XCT). XCT is a cross-sectional imaging method that has emerged as a derivative of optical coherence tomography (OCT). In contrast to OCT, which typically uses near-infrared light, XCT utilizes broad bandwidth extreme ultraviolet (XUV) and soft X-ray (SXR) radiation (Fuchs et al in Sci Rep 6:20658, 2016). As in OCT, XCT’s axial resolution only scales with the coherence length of the light source. Thus, an axial resolution down to the nanometer range can be achieved. This is an improvement of up to three orders of magnitude in comparison to OCT. XCT measures the reflected spectrum in a common-path interferometric setup to retrieve the axial structure of nanometer-sized samples. The technique has been demonstrated with broad bandwidth XUV/SXR radiation from synchrotron facilities and recently with compact laboratory-based laser-driven sources. Axial resolutions down to 2.2 nm have been achieved experimentally. XCT has potential applications in three-dimensional imaging of silicon-based semiconductors, lithography masks, and layered structures like XUV mirrors and solar cells.

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