scholarly journals Automated Screening for Abdominal Aortic Aneurysm in CT Scans under Clinical Conditions Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 2131
Author(s):  
Alena-K. Golla ◽  
Christian Tönnes ◽  
Tom Russ ◽  
Dominik F. Bauer ◽  
Matthias F. Froelich ◽  
...  
Keyword(s):  
Deep Learning ◽  
Aortic Aneurysms ◽  
Hospital Environment ◽  
Ct Scans ◽  
Training Dataset ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Abdominal Aortic ◽  
Data Set Integration

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research.

scholarly journals Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

Data ◽  
2018 ◽  
Vol 3 (3) ◽  
pp. 28 ◽  
Author(s):  
Kasthurirangan Gopalakrishnan
Keyword(s):  
Computer Vision ◽  
Image Analysis ◽  
Deep Learning ◽  
Asset Management ◽  
Network Architecture ◽  
Crack Detection ◽  
Future Research ◽  
Deep Convolutional Neural Networks ◽  
Pavement Distress ◽  
Learning Software

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.

scholarly journals Applying Deep Learning to Specific Learning Disorder Screening

2020 ◽  
Author(s):  
Nuriel Shalom Mor ◽  
Kathryn L Dardeck
Keyword(s):  
Deep Learning ◽  
Learning Disorder ◽  
Human Beings ◽  
Negative Consequences ◽  
Operating Characteristics ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Specific Learning Disorder ◽  
Deep Cnn ◽  
Specific Learning

Early detection is key for treating those diagnosed with specific learning disorder, which includes problems with spelling, grammar, punctuation, clarity and organization of written expression. Intervening early can prevent potential negative consequences from this disorder. Deep convolutional neural networks (CNNs) perform better than human beings in many visual tasks such as making a medical diagnosis from visual data. The purpose of this study was to evaluate the ability of a deep CNN to detect students with a diagnosis of specific learning disorder from their handwriting. The MobileNetV2 deep CNN architecture was used by applying transfer learning. The model was trained using a data set of 497 images of handwriting samples from students with a diagnosis of specific learning disorder, as well as those without this diagnosis. The detection of a specific learning disorder yielded on the validation set a mean area under the receiver operating characteristics curve of 0.89. This is a novel attempt to detect students with the diagnosis of specific learning disorder using deep learning. Such a system as was built for this study, may potentially provide fast initial screening of students who may meet the criteria for a diagnosis of specific learning disorder.

Covid-19 Detection using Deep Convolutional Neural Networks from X-Ray Images

2021 ◽  
Vol 4 (2) ◽  
pp. 139-143
Author(s):  
Abdullah Ajmal ◽  
Sundas Ibrar ◽  
Wakeel Ahmad ◽  
Syed Muhammad Adnan Shah
Keyword(s):  
Deep Learning ◽  
Global Economy ◽  
Data Augmentation ◽  
Diagnostic Techniques ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Daily Lives ◽  
X Ray ◽  
The World ◽  
Novel Coronavirus

Abstract— The Novel Coronavirus generally, knows as COVID-19 which first appeared in Wuhan city of China in December 2019, spread quickly around the world and became a pandemic. It has caused an overwhelming effect on daily lives, Public health, and the global economy. Many people have been affected and have died. It is critical to control and prevent the spread of COVID-19 disease by applying quick alternative diagnostic techniques. COVID-19 cases are rising day by day around the world, the on-time diagnosis of COVID-19 patients is an increasingly long and difficult process. COVID-19 patient test kits are costly and not available for every individual in poor countries. For this purpose, screening patients with the established techniques like Chest X-ray images seems to be an effective method. This study used a deep learning data augmentation on a publicly available data set and train advanced CNN models on it. The proposed model was tested using a state-of-the-art evaluation measures and obtained better results. Our model, the COVID-19 images is available at (https://github.com/ieee8023/covid-chestxray-dataset) and for Non-COVID-19 images is available at (https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia). The maximum accuracy achieved in the validation was 96.67%. Our model of COVID-19 detection achieved an average F measure of 98%, and an Area Under Curve (AUC) of 99%. The results demonstrate that deep learning proved to be an effective and easily deployable approach for COVID-19 detection.

Robust deep learning seismic inversion with a priori initial model constraint

2021 ◽  
Author(s):  
Jian Zhang ◽  
Jingye Li ◽  
Xiaohong Chen ◽  
Yuanqiang Li ◽  
Guangtan Huang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Seismic Data ◽  
A Priori ◽  
Seismic Inversion ◽  
Training Dataset ◽  
Initial Model ◽  
Data Set ◽  
The Real ◽  
Model Constraint ◽  
Trained Network

Summary Seismic inversion is one of the most commonly used methods in the oil and gas industry for reservoir characterization from observed seismic data. Deep learning (DL) is emerging as a data-driven approach that can effectively solve the inverse problem. However, existing deep learning-based methods for seismic inversion utilize only seismic data as input, which often leads to poor stability of the inversion results. Besides, it has always been challenging to train a robust network since the real survey has limited labeled data pairs. To partially overcome these issues, we develop a neural network framework with a priori initial model constraint to perform seismic inversion. Our network uses two parts as one input for training. One is the seismic data, and the other is the subsurface background model. The labels for each input are the actual model. The proposed method is performed by log-to-log strategy. The training dataset is firstly generated based on forward modeling. The network is then pre-trained using the synthetic training dataset, which is further validated using synthetic data that has not been used in the training step. After obtaining the pre-trained network, we introduce the transfer learning strategy to fine-tune the pre-trained network using labeled data pairs from a real survey to acquire better inversion results in the real survey. The validity of the proposed framework is demonstrated using synthetic 2D data including both post-stack and pre-stack examples, as well as a real 3D post-stack seismic data set from the western Canadian sedimentary basin.

scholarly journals CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242013
Author(s):  
Hongyu Wang ◽  
Hong Gu ◽  
Pan Qin ◽  
Jia Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Characteristic Curve ◽  
Healthcare Delivery ◽  
Chest Radiographs ◽  
Training Dataset ◽  
Deep Convolutional Neural Networks ◽  
Support Tool

Background Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs. Methods and findings We developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance. Conclusions In this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

scholarly journals Diabetic Retinopathy detection through integration of Deep Learning classification framework

2017 ◽  
Author(s):  
Alexander Rakhlin
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
State Of The Art ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Data Set ◽  
Diabetic Retinopathy Screening ◽  
Classification Framework ◽  
Ongoing Project ◽  
Retinopathy Screening

AbstractThis document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.

scholarly journals Dimensionality Reduction Techniques For Hyperspectral Image using Deep Learning

2019 ◽  
Vol 9 (2S3) ◽  
pp. 364-370
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Hyperspectral Image ◽  
Computational Cost ◽  
Research Field ◽  
Training Dataset ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Non Gaussian

This Research proposal addresses the issues of dimension reduction algorithms in Deep Learning(DL) for Hyperspectral Imaging (HSI) classification, to reduce the size of training dataset and for feature extraction ICA(Independent Component Analysis) are adopted. The proposed algorithm evaluated uses real HSI data set. It shows that ICA gives the most optimistic presentation it shrinks off the feature occupying a small portion of all pixels distinguished from the noisy bands based on non Gaussian assumption of independent sources. In turn, finding the independent components to address the challenge. A new approach DL based method is adopted, that has greater attention in the research field of HSI. DL based method is evaluated by a sequence prediction architecture that includes a recurrent neural network the LSTM architecture. It includes CNN layers for feature extraction of input datasets that have better accuracy with minimum computational cost

Outcome after open surgery repair in endovascular-suitable patients with ruptured abdominal aortic aneurysms

VASA ◽  
2013 ◽  
Vol 42 (6) ◽  
pp. 442-448 ◽  
Author(s):  
Felix Krenzien ◽  
Ivan Matia ◽  
Georg Wiltberger ◽  
Hans-Michael Hau ◽  
Bruno Freitas ◽  
...  
Keyword(s):  
Open Surgery ◽  
Endovascular Aneurysm Repair ◽  
Patient Characteristics ◽  
Abdominal Aortic Aneurysms ◽  
Aortic Aneurysms ◽  
Ct Scans ◽  
Abdominal Aortic ◽  
Preoperative Ct ◽  
Ruptured Abdominal Aortic Aneurysms ◽  
The Impact

Background: Endovascular aneurysm repair (EVAR) has been suggested in several studies to be superior to open surgery repair (OSR) for the treatment of ruptured abdominal aortic aneurysms (rAAAs), but this finding might be affected by selection bias based on aneurysm morphology and patient characteristics. We tested rAAA anatomy according to EVAR suitability in patients undergoing OSR to assess the impact on mortality. Patients and methods: This retrospective analysis reports on 83 patients with rAAAs treated between November 2002 and July 2013. Pre-operative computed tomography (CT) scans were evaluated based on EVAR suitability and were determined by blinded independent reviewers. CT scans were lacking due to acquisition in an external institution with no availability (n = 9) or solely ultrasound evaluations (n = 8). In addition patient characteristics and outcomes were assessed. Results: All patients who underwent OSR and who had available preoperative CT scans were included in the study (n = 66). In summary, 42 % of the patients (28/66; 95 % confidence interval [CI], 30.5 - 54.4) were considered eligible for EVAR according to pre-operative CT scans and 58 % of the patients (38/66; 95 % CI, 45.6 - 69.5) were categorized as unsuitable for endovascular repair. Patients suitable for EVAR had a significantly lower prevalence of in-hospital deaths (25 % [7/28]; 95 % CI, 9 - 41) in contrast to patients unsuitable for EVAR (53 % [20/38]; 95 % CI, 36.8 - 68.5; p = 0.02). Conclusions: EVAR-suitable patients had a highly significant mortality reduction undergoing OSR. Thus, the present study proposes that EVAR suitability is a positive predictor for survival after open repair of rAAA.

Development and validation of a deep learning model to assess tumor progression to immunotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20601-e20601 ◽  
Author(s):  
Yi Yang ◽  
Jiancheng Yang ◽  
Yuxiang Ye ◽  
Tian Xia ◽  
Shun Lu
Keyword(s):  
Deep Learning ◽  
Tumor Progression ◽  
Tumor Volume ◽  
Tumor Response ◽  
Learning Model ◽  
Ct Scans ◽  
Tumor Diameter ◽  
Data Set ◽  
Senior Clinician ◽  
Deep Learning Model

e20601 Background: Manual application of length-based tumor response criteria is the standard-of-care for assessing metastatic tumor response. It is technically challenging, time-consuming and associated with low reproducibility. In this study, we presented a novel automatic Deep Neural Networks (DNNs) based segmentation method for assessing tumor progression to immunotherapy. Next stage, AI will assist Physicians assessing pseudo-progression. Methods: A data set of 39 lung cancer patients with 156 computed tomography (CT) scans was used for model training and validation. A 3D segmentation DNN DenseSharp, was trained with an input size of on CT scans of tumor with manual delineated volume of interest (VOI) as ground truth. The trained model was subsequently used to estimate the volumes of target lesions via 16 sliding windows. We referred the progression-free survival (PFS) only considering tumor size as PFS-T. PFS-Ts assessed by longest tumor diameter (PFS-Tdiam), by tumor volume (PFS-Tvol), and by predicted tumor volume (PFS-Tpred-vol) were compared with standard PFS (as assessed by one junior and one senior clinician). Tumor progression was defined as > 20% increase in the longest tumor diameter or > 50% increase in tumor volume. Effective treatment was defined as a PFS of > 60 days after immunotherapy. Results: In a 4-fold cross-validation test, the DenseSharp segmentation neural network achieved a mean per-class intersection over union (mIoU) of 80.1%. The effectiveness rates of immunotherapy assessed using PFS-Tdiam (32 / 39, 82.1%), PFS-Tvol (33/39, 84.6%) and PFS-T pred-vol (32/39, 82.1%) were the same as standard PFS. The agreement between PFS-Tvol, and PFS-Tpred-vol was 97.4% (38/39). Evaluation time with deep learning model implemented with PyTorch 0.4.1 on GTX 1080 GPU was hundred-fold faster than manual evaluation (1.42s vs. 5-10 min per patient). Conclusions: In this study, DNN based model demonstrated fast and stable performance for tumor progression evaluation. Automatic volumetric measurement of tumor lesion enabled by deep learning provides the potential for a more efficient, objective and sensitive measurement than linear measurement by clinicians.

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