S-CCCapsule: Pneumonia detection in chest X-ray images using skip-connected convolutions and capsule neural network

2021 ◽  
pp. 1-25
Author(s):  
Kwabena Adu ◽  
Yongbin Yu ◽  
Jingye Cai ◽  
Victor Dela Tattrah ◽  
James Adu Ansere ◽  
...  
Keyword(s):  
Neural Network ◽  
Human Error ◽  
Medical Center ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Dynamic Routing ◽  
Sigmoid Function ◽  
Radiological Society ◽  
Chest X Ray ◽  
Sensitivity Specificity

The squash function in capsule networks (CapsNets) dynamic routing is less capable of performing discrimination of non-informative capsules which leads to abnormal activation value distribution of capsules. In this paper, we propose vertical squash (VSquash) to improve the original squash by preventing the activation values of capsules in the primary capsule layer to shrink non-informative capsules, promote discriminative capsules and avoid high information sensitivity. Furthermore, a new neural network, (i) skip-connected convolutional capsule (S-CCCapsule), (ii) Integrated skip-connected convolutional capsules (ISCC) and (iii) Ensemble skip-connected convolutional capsules (ESCC) based on CapsNets are presented where the VSquash is applied in the dynamic routing. In order to achieve uniform distribution of coupling coefficient of probabilities between capsules, we use the Sigmoid function rather than Softmax function. Experiments on Guangzhou Women and Children’s Medical Center (GWCMC), Radiological Society of North America (RSNA) and Mendeley CXR Pneumonia datasets were performed to validate the effectiveness of our proposed methods. We found that our proposed methods produce better accuracy compared to other methods based on model evaluation metrics such as confusion matrix, sensitivity, specificity and Area under the curve (AUC). Our method for pneumonia detection performs better than practicing radiologists. It minimizes human error and reduces diagnosis time.

scholarly journals Mooring-Failure Monitoring of Submerged Floating Tunnel Using Deep Neural Network

2020 ◽  
Vol 10 (18) ◽  
pp. 6591
Author(s):  
Do-Soo Kwon ◽  
Chungkuk Jin ◽  
MooHyun Kim ◽  
Weoncheol Koo
Keyword(s):  
Neural Network ◽  
Classification Accuracy ◽  
Deep Neural Network ◽  
Confusion Matrix ◽  
Target Prediction ◽  
Random Wave ◽  
Motion Sensors ◽  
Sigmoid Function ◽  
Submerged Floating Tunnel ◽  
Sensor Signals

This paper presents a machine learning method for detecting the mooring failures of SFT (submerged floating tunnel) based on DNN (deep neural network). The floater-mooring-coupled hydro-elastic time-domain numerical simulations are conducted under various random wave excitations and failure/intact scenarios. Then, the big-data is collected at various locations of numerical motion sensors along the SFT to be used for the present DNN algorithm. In the input layer, tunnel motion-sensor signals and wave conditions are inputted while the output layer provides the probabilities of 21 failure scenarios. In the optimization stage, the numbers of hidden layers, neurons of each layer, and epochs for reliable performance are selected. Several activation functions and optimizers are also tested for the present DNN model, and Sigmoid function and Adamax are respectively adopted to enhance the classification accuracy. Moreover, a systematic sensitivity test with respect to the numbers and arrangements of sensors is performed to find the appropriate sensor combination to achieve target prediction accuracy. The technique of confusion matrix is used to represent the accuracy of the DNN algorithms for various cases, and the classification accuracy as high as 98.1% is obtained with seven sensors. The results of this study demonstrate that the DNN model can effectively monitor the mooring failures of SFTs utilizing real-time sensor signals.

scholarly journals Artificial Intelligence Applied to Chest X-ray for Differential Diagnosis of COVID-19 Pneumonia

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 530
Author(s):  
Christian Salvatore ◽  
Matteo Interlenghi ◽  
Caterina B. Monti ◽  
Davide Ippolito ◽  
Davide Capra ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Area Under The Curve ◽  
Community Acquired Pneumonia ◽  
Validation Dataset ◽  
X Rays ◽  
Different Types ◽  
Chest X Ray ◽  
Sensitivity Specificity

We assessed the role of artificial intelligence applied to chest X-rays (CXRs) in supporting the diagnosis of COVID-19. We trained and cross-validated a model with an ensemble of 10 convolutional neural networks with CXRs of 98 COVID-19 patients, 88 community-acquired pneumonia (CAP) patients, and 98 subjects without either COVID-19 or CAP, collected in two Italian hospitals. The system was tested on two independent cohorts, namely, 148 patients (COVID-19, CAP, or negative) collected by one of the two hospitals (independent testing I) and 820 COVID-19 patients collected by a multicenter study (independent testing II). On the training and cross-validation dataset, sensitivity, specificity, and area under the curve (AUC) were 0.91, 0.87, and 0.93 for COVID-19 versus negative subjects, 0.85, 0.82, and 0.94 for COVID-19 versus CAP. On the independent testing I, sensitivity, specificity, and AUC were 0.98, 0.88, and 0.98 for COVID-19 versus negative subjects, 0.97, 0.96, and 0.98 for COVID-19 versus CAP. On the independent testing II, the system correctly diagnosed 652 COVID-19 patients versus negative subjects (0.80 sensitivity) and correctly differentiated 674 COVID-19 versus CAP patients (0.82 sensitivity). This system appears promising for the diagnosis and differential diagnosis of COVID-19, showing its potential as a second opinion tool in conditions of the variable prevalence of different types of infectious pneumonia.

Early Prediction of Parkinson’s Disease from Brain MRI Images Using Convolutional Neural Network

2021 ◽  
Vol 11 (12) ◽  
pp. 3103-3109
Author(s):  
G. Prema Arokia Mary ◽  
N. Suganthi ◽  
M. S. Hema
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Human Error ◽  
Brain Mri ◽  
Early Prediction ◽  
Healthy Individuals ◽  
Automatic Feature Extraction ◽  
Sensitivity Specificity ◽  
The Brain

The early diagnosis of Parkinson’s Disease (PD) is a challenging practice for doctors. Currently, there are no separate diagnostics and tests to be done to predict onset PD. However, the PD can be predicted through repeated clinical trials and tests. Sometimes, early prediction of PD can become tedious based on trials and tests. The computer-aided prediction will help medical professionals predict PD accurately during one’s onset stages to improve the PD patients’ quality of life. Hence, early prediction of PD is essential. In this article, Convolution Neural Networks (CNN) is proposed to classify PD patients and healthy individuals. The brain MRI images are given as input for the proposed methodology. The CNN deep neural network will first extract the features from the images. Then, it will classify the PD patients and healthy individuals from the extracted features. The automatic feature extraction will improve the accuracy of the classifier and reduce human error. The brain MRI images are taken from the PPMI dataset for experimentation. The sensitivity, specificity, and accuracy are calculated to assess the performance of the proposed methodology. The loss is also calculated to verify the performance of the classifier. It is observed that the CNN classifier has produced a higher accuracy of more than 98% in classifying PD patients and healthy individuals when compared to multi-layer perceptron deep learning.

scholarly journals An App Developed for Detecting Nurse Burnouts Using the Convolutional Neural Networks in Microsoft Excel: Population-Based Questionnaire Study (Preprint)

2019 ◽  
Author(s):  
Yi-Lien Lee ◽  
Willy Chou ◽  
Tsair-Wei Chien ◽  
Po-Hsin Chou ◽  
Yu-Tsen Yeh ◽  
...  
Keyword(s):  
Predictive Accuracy ◽  
Early Stage ◽  
Medical Center ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Maslach Burnout Inventory ◽  
Well Being ◽  
Population Based ◽  
Psychological Status ◽  
Sensitivity Specificity

BACKGROUND Burnout (BO), a critical syndrome particularly for nurses in health care settings, substantially affects their physical and psychological status, the institute’s well-being, and indirectly, patient outcomes. However, objectively classifying BO levels has not been defined and noticed in the literature. OBJECTIVE The aim of this study is to build a model using the convolutional neural network (CNN) to develop an app for automatic detection and classification of nurse BO using the Maslach Burnout Inventory–Human Services Survey (MBI-HSS) to help assess nurse BO at an earlier stage. METHODS We recruited 1002 nurses working in a medical center in Taiwan to complete the Chinese version of the 20-item MBI-HSS in August 2016. The k-mean and CNN were used as unsupervised and supervised learnings for dividing nurses into two classes (n=531 and n=471 of suspicious BO+ and BO−, respectively) and building a BO predictive model to estimate 38 parameters. Data were separated into training and testing sets in a proportion 70%:30%, and the former was used to predict the latter. We calculated the sensitivity, specificity, and receiver operating characteristic curve (area under the curve) across studies for comparison. An app predicting respondent BO was developed involving the model’s 38 estimated parameters for a website assessment. RESULTS We observed that (1) the 20-item model yields a higher accuracy rate (0.95) with an area under the curve of 0.97 (95% CI 0.94-0.95) based on the 1002 cases, (2) the scheme named matching personal response to adapt for the correct classification in model drives the prior model’s predictive accuracy at 100%, (3) the 700-case training set with 0.96 accuracy predicts the 302-case testing set reaching an accuracy of 0.91, and (4) an available MBI-HSS app for nurses predicting BO was successfully developed and demonstrated in this study. CONCLUSIONS The 20-item model with the 38 parameters estimated by using CNN for improving the accuracy of nurse BO has been particularly demonstrated in Excel (Microsoft Corp). An app developed for helping nurses to self-assess job BO at an early stage is required for application in the future.

scholarly journals Automated Chest Radiographs Triage Reading by a Deep Learning Referee Network

2021 ◽  
Author(s):  
Rafael Lopez-Gonzalez ◽  
Jose Sanchez-Garcia ◽  
Belen Fos-Guarinos ◽  
Fabio Garcia-Castro ◽  
Angel Alberich-Bayarri ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Language Processing ◽  
Large Scale ◽  
Area Under The Curve ◽  
Chest Radiographs ◽  
Radiology Reports ◽  
Chest X Ray ◽  
Diagnosis Tool ◽  
Fully Connected

Chest radiographs are often obtained as a screening for early diagnosis tool to rule out abnormalities mainly related to different cardiovascular and respiratory diseases. Reading and reporting numerous chest radiographs is a complex and time-consuming task. This research proposes and evaluates a deep learning (DL) approach based on convolutional neural networks (CNN) combined with a referee fully connected neural network as a computer-aided diagnosis tool in chest X-ray triage and worklist prioritization. The CNN models were trained with a combination of three large scale databases: ChestX-ray14, CheXpert and PadChest. The final database contained 327,176 images labeled with findings obtained by natural language processing (NLP) techniques applied to the radiology reports. The dataset was split in 16 different balanced binary partitions, which were used to train 16 finding-specific classification CNNs. Afterwards, a normal vs abnormal partition of the dataset was created, being abnormal the presence of at least one pathologic change. This final partition was used to train a fully connected neural network as referee that was fed with all the 16 previously trained outcomes. The Area Under the Curve (AUC) analysis evaluated and compared the performance of the models. The system was successfully implemented and evaluated with a test set of 3400 images. The AUC of the normal vs abnormal classification was 0.94. The highest AUC of the finding-specific classifiers was 0.99 for hernia. The proposed system can be used to assist radiologists identifying abnormal exams, allowing a time-efficiency triage approach.

scholarly journals Accuracy Assessment in Convolutional Neural Network-Based Deep Learning Remote Sensing Studies—Part 1: Literature Review

2021 ◽  
Vol 13 (13) ◽  
pp. 2450
Author(s):  
Aaron E. Maxwell ◽  
Timothy A. Warner ◽  
Luis Andrés Guillén
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Accuracy Assessment ◽  
Confusion Matrix ◽  
Area Under The Curve ◽  
Average Precision ◽  
Wide Range ◽  
New Sensors

Convolutional neural network (CNN)-based deep learning (DL) is a powerful, recently developed image classification approach. With origins in the computer vision and image processing communities, the accuracy assessment methods developed for CNN-based DL use a wide range of metrics that may be unfamiliar to the remote sensing (RS) community. To explore the differences between traditional RS and DL RS methods, we surveyed a random selection of 100 papers from the RS DL literature. The results show that RS DL studies have largely abandoned traditional RS accuracy assessment terminology, though some of the accuracy measures typically used in DL papers, most notably precision and recall, have direct equivalents in traditional RS terminology. Some of the DL accuracy terms have multiple names, or are equivalent to another measure. In our sample, DL studies only rarely reported a complete confusion matrix, and when they did so, it was even more rare that the confusion matrix estimated population properties. On the other hand, some DL studies are increasingly paying attention to the role of class prevalence in designing accuracy assessment approaches. DL studies that evaluate the decision boundary threshold over a range of values tend to use the precision-recall (P-R) curve, the associated area under the curve (AUC) measures of average precision (AP) and mean average precision (mAP), rather than the traditional receiver operating characteristic (ROC) curve and its AUC. DL studies are also notable for testing the generalization of their models on entirely new datasets, including data from new areas, new acquisition times, or even new sensors.

Classification Approach for Breast Cancer Detection Using Back Propagation Neural Network

2020 ◽  
pp. 1410-1421 ◽  
Author(s):  
Aindrila Bhattacherjee ◽  
Sourav Roy ◽  
Sneha Paul ◽  
Payel Roy ◽  
Noreen Kausar ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Confusion Matrix ◽  
Back Propagation ◽  
Breast Cancer Diagnosis ◽  
Back Propagation Neural Network ◽  
Classification Systems ◽  
Breast Cancer Dataset ◽  
Cancer Dataset ◽  
Sensitivity Specificity

According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%. According to the recent surveys, breast cancer has become one of the major causes of mortality rate among women. Breast cancer can be defined as a group of rapidly growing cells that lead to the formation of a lump or an extra mass in the breast tissue which consequently leads to the formation of tumor. Tumors can be classified as malignant (cancerous) or benign (non-cancerous). Feature selection is an important parameter in determining the classification systems. Machine learning methods are the most commonly used methods among researchers for breast cancer diagnosis. This paper proposes to investigate the WBCD (Wisconsin Breast Cancer Dataset) which comprises of 683 patients and implements the chosen features to train the back propagation neural network. The performance is then analyzed on the basis of classification accuracy, sensitivity, specificity, positive and negative predictor values, receiver operating characteristic curves and confusion matrix. A total of 9 features has been used to classify breast cancer with an accuracy of 99.27%.

Automatic detection of colorectal neoplasia in wireless colon capsule endoscopic images using a deep convolutional neural network

Endoscopy ◽  
2020 ◽  
Author(s):  
Atsuo Yamada ◽  
Ryota Niikura ◽  
Keita Otani ◽  
Tomonori Aoki ◽  
Kazuhiko Koike
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Colorectal Neoplasms ◽  
Colorectal Neoplasia ◽  
Area Under The Curve ◽  
Deep Convolutional Neural Network ◽  
Single Shot ◽  
Colon Capsule Endoscopy ◽  
Colon Capsule ◽  
Sensitivity Specificity

Abstract Background Although colorectal neoplasms are the most common abnormalities found in colon capsule endoscopy (CCE), no computer-aided detection method is yet available. We developed an artificial intelligence (AI) system that uses deep learning to automatically detect such lesions in CCE images. Methods We trained a deep convolutional neural network system based on a Single Shot MultiBox Detector using 15 933 CCE images of colorectal neoplasms, such as polyps and cancers. We assessed performance by calculating areas under the receiver operating characteristic curves, along with sensitivities, specificities, and accuracies, using an independent test set of 4784 images, including 1850 images of colorectal neoplasms and 2934 normal colon images. Results The area under the curve for detection of colorectal neoplasia by the AI model was 0.902. The sensitivity, specificity, and accuracy were 79.0 %, 87.0 %, and 83.9 %, respectively, at a probability cutoff of 0.348. Conclusions We developed and validated a new AI-based system that automatically detects colorectal neoplasms in CCE images.

scholarly journals Deep learning for screening of pulmonary hypertension using standard chest X-ray

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y Hirata ◽  
K Kusunose ◽  
N Yamaguchi ◽  
S Morita ◽  
S Nishio ◽  
...  
Keyword(s):  
Neural Network ◽  
Pulmonary Hypertension ◽  
Pulmonary Artery ◽  
Pulmonary Artery Pressure ◽  
Low Cost ◽  
Area Under The Curve ◽  
Independent Set ◽  
X Ray ◽  
Artery Pressure ◽  
Chest X Ray

Abstract Background Early detection of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment for the progressive clinical course. The chest X-ray (CXR), a routine method at hospitals, has recommended in order to reveal features supportive of a diagnosis of PH. However, it is well known that the sensitivity and specificity are low. Purpose We tested the hypothesis that application of artificial intelligence (AI) to the CXR could identify PH. Methods We retrospectively enrolled 900 data with paired CXR and right heart catheter (RHC), including the pulmonary artery pressure, from October 2009 to December 2018. We trained a convolutional neural network to identify patients with PH as actual value of pulmonary artery pressure, using the CXR alone (Figure). The diagnosis of PH was performed using hemodynamic measurements according to the most recent World Symposium standards: mean PAP ≥20 mmHg. We have compared the area under the curve (AUC) by human observers, measurements of CXR images, and AI for detection of PH. Results Subjects were divided into two groups with PH (439 patients; mean age, 66±14 years; 233 male) and without PH (461 patients; mean age, 68±12 years; 278 male). In an independent set, AI was the highest diagnostic ability for detection of PH (AUC: 0.71). The AUC by the AI algorithm was significantly higher than the AUC by measurements of CXR images and human observers (0.71 vs. 0.60 and vs. 0.63, all compared p<0.05). Conclusion Applying AI to the CXR (a classical, universal, low-cost test) permits the CXR images to serve as a powerful tool to screen for PH. Neural network Funding Acknowledgement Type of funding source: None

scholarly journals Deep Learning Network Anomaly-Based Intrusion Detection Ensemble For Predictive Intelligence To Curb Malicious Connections: An Empirical Evidence

2021 ◽  
Vol 10 (3) ◽  
pp. 2090-2102
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Communication Technology ◽  
Driving Force ◽  
Confusion Matrix ◽  
Hybrid Genetic Algorithm ◽  
Process Data ◽  
Learning Network ◽  
Deep Learning Network ◽  
Sensitivity Specificity

The future is always beenshaped and refocused via Sci-Tech. Info and communication technology –has continued to shape today’s society as an inevitable driving force because we are now heavily dependent on digitally transmitted and processed data. This, is consequent upon the fact that individuals and organizations are seeking improve means to process data more effectively and efficiently. We thus, propose hybrid Genetic Algorithm trained Modular Neural Network to detect network anomaly cum malicious packets. GA was used due to its flexibility cum elitist mode. MNN is used as a learning paradigm for modular learning components. Model validation return a confusion matrix with these values: TP = 50, TN = 2, FN = 5, FP = 3. These values were subsequently applied to obtain sensitivity,specificity and accuracy of model. Model portrays a sensitivity value of 93%, specificity value of 25% and an accuracy value of 89%.

Sign in / Sign up

Export Citation Format

Share Document