Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays

<div><div><div><p>Among radiological imaging data, chest X-rays are of great use in observing COVID-19 mani- festations. For mass screening, using chest X-rays, a computationally efficient AI-driven tool is the must to detect COVID-19 positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19 positive cases using chest X-rays, with no false positive. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models, which was validated using 130 COVID-19 positive chest X-rays. In this study, in addition to COVID-19 positive cases, another set of non-COVID-19 cases (exactly similar to the size of COVID-19 set) was taken into account, where MERS, SARS, Pneumonia, and healthy chest X-rays were used. In experimental tests, to avoid possible bias, 5-fold cross validation was followed. Using 260 chest X-rays, the proposed model achieved an accuracy of an accuracy of 96.92%, sensitivity of 0.942, where AUC was 0.9869. Further, the reported false positive rate was 0 for 130 COVID-19 positive cases. This stated that proposed tool could possibly be used for mass screening. Note to be confused, it does not include any clinical implications. Using the exact same set of chest X-rays collection, the current results were better than other deep learning models and state-of-the-art works.</p></div></div></div>

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Shallow Convolutional Neural Network for COVID-19 Outbreak Screening using Chest X-rays

10.36227/techrxiv.12156522.v1 ◽

2020 ◽

Cited By ~ 6

Author(s):

Himadri Mukherjee ◽

Subhankar Ghosh ◽

Ankita Dhar ◽

Sk. Md. Obaidullah ◽

KC Santosh ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Mass Screening ◽

False Positive ◽

False Positive Rate ◽

Experimental Tests ◽

X Rays ◽

Imaging Data ◽

Learning Models

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Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

Applied Sciences ◽

10.3390/app11157147 ◽

2021 ◽

Vol 11 (15) ◽

pp. 7147

Author(s):

Jinmo Gu ◽

Jinhyuk Na ◽

Jeongeun Park ◽

Hayoung Kim

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

False Positive Rate ◽

Direct Marketing ◽

Network Models ◽

Learning Models ◽

Insurance Policy ◽

Neural Network Models ◽

Potential Customers

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

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Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

JMIR Medical Informatics ◽

10.2196/24973 ◽

2021 ◽

Vol 9 (1) ◽

pp. e24973

Author(s):

Thao Thi Ho ◽

Jongmin Park ◽

Taewoo Kim ◽

Byunggeon Park ◽

Jaehee Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

High Risk ◽

Convolutional Neural Network ◽

Clinical Data ◽

Area Under The Curve ◽

Classification Performance ◽

Imaging Data ◽

Learning Models ◽

Predictive Tool

Background Many COVID-19 patients rapidly progress to respiratory failure with a broad range of severities. Identification of high-risk cases is critical for early intervention. Objective The aim of this study is to develop deep learning models that can rapidly identify high-risk COVID-19 patients based on computed tomography (CT) images and clinical data. Methods We analyzed 297 COVID-19 patients from five hospitals in Daegu, South Korea. A mixed artificial convolutional neural network (ACNN) model, combining an artificial neural network for clinical data and a convolutional neural network for 3D CT imaging data, was developed to classify these cases as either high risk of severe progression (ie, event) or low risk (ie, event-free). Results Using the mixed ACNN model, we were able to obtain high classification performance using novel coronavirus pneumonia lesion images (ie, 93.9% accuracy, 80.8% sensitivity, 96.9% specificity, and 0.916 area under the curve [AUC] score) and lung segmentation images (ie, 94.3% accuracy, 74.7% sensitivity, 95.9% specificity, and 0.928 AUC score) for event versus event-free groups. Conclusions Our study successfully differentiated high-risk cases among COVID-19 patients using imaging and clinical features. The developed model can be used as a predictive tool for interventions in aggressive therapies.

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Validating Deep Neural Networks for Online Decoding of Motor Imagery Movements from EEG Signals

Sensors ◽

10.3390/s19010210 ◽

2019 ◽

Vol 19 (1) ◽

pp. 210 ◽

Cited By ~ 32

Author(s):

Zied Tayeb ◽

Juri Fedjaev ◽

Nejla Ghaboosi ◽

Christoph Richter ◽

Lukas Everding ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Convolutional Neural Network ◽

Motor Imagery ◽

Classification Performance ◽

Feature Engineering ◽

Learning Models ◽

Eeg Signals ◽

Learning Methods

Non-invasive, electroencephalography (EEG)-based brain-computer interfaces (BCIs) on motor imagery movements translate the subject’s motor intention into control signals through classifying the EEG patterns caused by different imagination tasks, e.g., hand movements. This type of BCI has been widely studied and used as an alternative mode of communication and environmental control for disabled patients, such as those suffering from a brainstem stroke or a spinal cord injury (SCI). Notwithstanding the success of traditional machine learning methods in classifying EEG signals, these methods still rely on hand-crafted features. The extraction of such features is a difficult task due to the high non-stationarity of EEG signals, which is a major cause by the stagnating progress in classification performance. Remarkable advances in deep learning methods allow end-to-end learning without any feature engineering, which could benefit BCI motor imagery applications. We developed three deep learning models: (1) A long short-term memory (LSTM); (2) a spectrogram-based convolutional neural network model (CNN); and (3) a recurrent convolutional neural network (RCNN), for decoding motor imagery movements directly from raw EEG signals without (any manual) feature engineering. Results were evaluated on our own publicly available, EEG data collected from 20 subjects and on an existing dataset known as 2b EEG dataset from “BCI Competition IV”. Overall, better classification performance was achieved with deep learning models compared to state-of-the art machine learning techniques, which could chart a route ahead for developing new robust techniques for EEG signal decoding. We underpin this point by demonstrating the successful real-time control of a robotic arm using our CNN based BCI.

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Efficient Facial Emotion Recognition Model using Deep Convolutional Neural Network and Modified Joint Trilateral Filter

10.21203/rs.3.rs-866042/v1 ◽

2021 ◽

Author(s):

Naveen Kumari ◽

Rekha Bhatia

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Emotion Recognition ◽

Deep Convolutional Neural Network ◽

Facial Emotion Recognition ◽

Facial Emotion ◽

Learning Models ◽

Recognition Model ◽

Trilateral Filter

Abstract Facial emotion recognition extracts the human emotions from the images and videos. As such, it requires an algorithm to understand and model the relationships between faces and facial expressions, and to recognize human emotions. Recently, deep learning models are extensively utilized enhance the facial emotion recognition rate. However, the deep learning models suffer from the overfitting issue. Moreover, deep learning models perform poorly for images which have poor visibility and noise. Therefore, in this paper, a novel deep learning based facial emotion recognition tool is proposed. Initially, a joint trilateral filter is applied to the obtained dataset to remove the noise. Thereafter, contrast-limited adaptive histogram equalization (CLAHE) is applied to the filtered images to improve the visibility of images. Finally, a deep convolutional neural network is trained. Nadam optimizer is also utilized to optimize the cost function of deep convolutional neural networks. Experiments are achieved by using the benchmark dataset and competitive human emotion recognition models. Comparative analysis demonstrates that the proposed facial emotion recognition model performs considerably better compared to the competitive models.

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Recognition of Urdu Handwritten Characters Using Convolutional Neural Network

Applied Sciences ◽

10.3390/app9132758 ◽

2019 ◽

Vol 9 (13) ◽

pp. 2758 ◽

Cited By ~ 7

Author(s):

Mujtaba Husnain ◽

Malik Muhammad Saad Missen ◽

Shahzad Mumtaz ◽

Muhammad Zeeshan Jhanidr ◽

Mickaël Coustaty ◽

...

Keyword(s):

Neural Network ◽

Pattern Recognition ◽

Deep Learning ◽

Convolutional Neural Network ◽

Pattern Matching ◽

Character Recognition ◽

Learning Models ◽

Series Of Experiments

In the area of pattern recognition and pattern matching, the methods based on deep learning models have recently attracted several researchers by achieving magnificent performance. In this paper, we propose the use of the convolutional neural network to recognize the multifont offline Urdu handwritten characters in an unconstrained environment. We also propose a novel dataset of Urdu handwritten characters since there is no publicly-available dataset of this kind. A series of experiments are performed on our proposed dataset. The accuracy achieved for character recognition is among the best while comparing with the ones reported in the literature for the same task.

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OTHR-13. A DEEP LEARNING APPROACH TO DETECT CANCER METASTASES TO THE BRAIN IN MRI

Neuro-Oncology Advances ◽

10.1093/noajnl/vdz014.090 ◽

2019 ◽

Vol 1 (Supplement_1) ◽

pp. i20-i21

Author(s):

Min Zhang ◽

Geoffrey Young ◽

Huai Chen ◽

Lei Qin ◽

Xinhua Cao ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Brain Metastases ◽

False Positive ◽

False Positive Rate ◽

Brain Mri ◽

Ground Truth ◽

Learning Approach ◽

Cancer Metastases ◽

The Brain

Abstract BACKGROUND AND OBJECTIVE: Brain metastases have been found to account for one-fourth of all cancer metastases seen in clinics. Magnetic resonance imaging (MRI) is widely used for detecting brain metastases. Accurate detection of the brain metastases is critical to design radiotherapy to treat the cancer and monitor their progression or response to the therapy and prognosis. However, finding metastases on brain MRI is very challenging as many metastases are small and manifest as objects of weak contrast on the images. In this work we present a deep learning approach integrated with a classification scheme to detect cancer metastases to the brain on MRI. MATERIALS AND METHODS: We retrospectively extracted 101 metastases patients, equal to 1535 metastases on 10192 slices of images in a total of 336 scans from our PACS and manually marked the lesions on T1-weighted contrast enhanced MRI as the ground-truth. We then randomly separated the cases into training, validation, and test sets for developing and optimizing the deep learning neural network. We designed a 2-step computer-aided detection (CAD) pipeline by first applying a fast region-based convolutional neural network method (R-CNN) to sequentially process each slice of an axial brain MRI to find abnormal hyper-intensity that may correspond to a brain metastasis and, second, applying a random under sampling boost (RUSBoost) classification method to reduce the false positive metastases. RESULTS: The computational pipeline was tested on real brain images. A sensitivity of 97.28% and false positive rate of 36.25 per scan over the images were achieved by using the proposed method. CONCLUSION: Our results demonstrated the deep learning-based method can detect metastases in very challenging cases and can serve as CAD tool to help radiologists interpret brain MRIs in a time-constrained environment.

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Sign Board Recognition Based on Convolutional Neural Network Using Yolo-3

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2020.9214 ◽

2020 ◽

Vol 17 (8) ◽

pp. 3478-3483

Author(s):

V. Sravan Chowdary ◽

G. Penchala Sai Teja ◽

D. Mounesh ◽

G. Manideep ◽

C. T. Manimegalai

Keyword(s):

Neural Network ◽

Deep Learning ◽

Object Detection ◽

Convolutional Neural Network ◽

Road Accidents ◽

Learning Models ◽

The Neural Network

Road injuries are a big drawback in society for a few time currently. Ignoring sign boards while moving on roads has significantly become a major cause for road accidents. Thus we came up with an approach to face this issue by detecting the sign board and recognition of sign board. At this moment there are several deep learning models for object detection using totally different algorithms like RCNN, faster RCNN, SPP-net, etc. We prefer to use Yolo-3, which improves the speed and precision of object detection. This algorithm will increase the accuracy by utilizing residual units, skip connections and up-sampling. This algorithm uses a framework named Dark-net. This framework is intended specifically to create the neural network for training the Yolo algorithm. To thoroughly detect the sign board, we used this algorithm.

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An Effective Convolutional Neural Network Model for the Early Detection of COVID-19 Using Chest X-ray Images

Applied Sciences ◽

10.3390/app112110301 ◽

2021 ◽

Vol 11 (21) ◽

pp. 10301

Author(s):

Muhammad Shoaib Farooq ◽

Attique Ur Rehman ◽

Muhammad Idrees ◽

Muhammad Ahsan Raza ◽

Jehad Ali ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Real World ◽

Early Stage ◽

Binary Classification ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.

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Application of deep learning models for aspect based sentiment analysis.

Bulletin of Bryansk state technical university ◽

10.12737/22917 ◽

2015 ◽

Vol 2015 (3) ◽

pp. 117-126

Author(s):

Дмитрий Будыльский ◽

Dmitriy Budylskiy ◽

Александр Подвесовский ◽

Aleksandr Podvesovskiy

Keyword(s):

Neural Network ◽

Deep Learning ◽

Natural Language Processing ◽

Convolutional Neural Network ◽

Language Processing ◽

Recurrent Neural Network ◽

Russian Text ◽

Learning Models ◽

Actual Problem ◽

Good Efficiency

This paper describes actual problem of sentiment based aspect analysis and four deep learning models: convolutional neural network, recurrent neural network, GRU and LSTM networks. We evaluated these models on Russian text dataset from SentiRuEval-2015. Results show good efficiency and high potential for further natural language processing applications.

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