COVID-19 Diagnosis Using an Enhanced Inception-ResNetV2 Deep Learning Model in CXR Images

The COVID-19 pandemic has a significant negative effect on people’s health, as well as on the world’s economy. Polymerase chain reaction (PCR) is one of the main tests used to detect COVID-19 infection. However, it is expensive, time-consuming, and lacks sufficient accuracy. In recent years, convolutional neural networks have grabbed many researchers’ attention in the machine learning field, due to its high diagnosis accuracy, especially the medical image recognition. Many architectures such as Inception, ResNet, DenseNet, and VGG16 have been proposed and gained an excellent performance at a low computational cost. Moreover, in a way to accelerate the training of these traditional architectures, residual connections are combined with inception architecture. Therefore, many hybrid architectures such as Inception-ResNetV2 are further introduced. This paper proposes an enhanced Inception-ResNetV2 deep learning model that can diagnose chest X-ray (CXR) scans with high accuracy. Besides, a Grad-CAM algorithm is used to enhance the visualization of the infected regions of the lungs in CXR images. Compared with state-of-the-art methods, our proposed paper proves superiority in terms of accuracy, recall, precision, and F1-measure.

Download Full-text

Improving Sentiment Analysis using Hybrid Deep Learning Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190328200012 ◽

2020 ◽

Vol 13 (4) ◽

pp. 627-640 ◽

Cited By ~ 1

Author(s):

Avinash Chandra Pandey ◽

Dharmveer Singh Rajpoot

Keyword(s):

Neural Network ◽

Deep Learning ◽

Sentiment Analysis ◽

Classification Accuracy ◽

Short Term Memory ◽

Computational Cost ◽

Extraction Process ◽

Learning Model ◽

Sentiment Classification ◽

Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Download Full-text

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105674 ◽

2020 ◽

Vol 197 ◽

pp. 105674

Author(s):

Dingding Yu ◽

Kaijie Zhang ◽

Lingyan Huang ◽

Bonan Zhao ◽

Xiaoshan Zhang ◽

...

Keyword(s):

Deep Learning ◽

Learning Model ◽

Peripherally Inserted Central Catheter ◽

Central Catheter ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Download Full-text

Accessory pathway analysis using a multimodal deep learning model

Scientific Reports ◽

10.1038/s41598-021-87631-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Makoto Nishimori ◽

Kunihiko Kiuchi ◽

Kunihiro Nishimura ◽

Kengo Kusano ◽

Akihiro Yoshida ◽

...

Keyword(s):

Deep Learning ◽

Electrophysiological Study ◽

Accessory Pathway ◽

Learning Model ◽

X Rays ◽

X Ray ◽

Wpw Syndrome ◽

Chest X Ray ◽

Deep Learning Model ◽

Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

Download Full-text

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

European Radiology Experimental ◽

10.1186/s41747-020-00203-z ◽

2021 ◽

Vol 5 (1) ◽

Cited By ~ 1

Author(s):

Isabella Castiglioni ◽

Davide Ippolito ◽

Matteo Interlenghi ◽

Caterina Beatrice Monti ◽

Christian Salvatore ◽

...

Keyword(s):

Deep Learning ◽

Area Under The Curve ◽

Clinical Information ◽

Training Dataset ◽

Independent Dataset ◽

X Ray ◽

Learning Classifier ◽

Chest X Ray ◽

Polymerase Chain ◽

Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

Download Full-text

COVID-19 Detection Using Chest X-Ray Images with a RegNet Structured Deep Learning Model

Applied Intelligence and Informatics - Communications in Computer and Information Science ◽

10.1007/978-3-030-82269-9_28 ◽

2021 ◽

pp. 358-370

Author(s):

Md. Kawsher Mahbub ◽

Milon Biswas ◽

Abdul Mozid Miah ◽

Ahmed Shahabaz ◽

M. Shamim Kaiser

Keyword(s):

Deep Learning ◽

Learning Model ◽

X Ray ◽

Chest X Ray ◽

Deep Learning Model

Download Full-text

ECOVNet: a highly effective ensemble based deep learning model for detecting COVID-19

PeerJ Computer Science ◽

10.7717/peerj-cs.551 ◽

2021 ◽

Vol 7 ◽

pp. e551

Author(s):

Nihad Karim Chowdhury ◽

Muhammad Ashad Kabir ◽

Md. Muhtadir Rahman ◽

Noortaz Rezoana

Keyword(s):

Deep Learning ◽

Detection System ◽

Learning Model ◽

Classification Performance ◽

Data Sets ◽

X Rays ◽

Proposed Model ◽

Highly Effective ◽

Chest X Ray ◽

Deep Learning Model

The goal of this research is to develop and implement a highly effective deep learning model for detecting COVID-19. To achieve this goal, in this paper, we propose an ensemble of Convolutional Neural Network (CNN) based on EfficientNet, named ECOVNet, to detect COVID-19 from chest X-rays. To make the proposed model more robust, we have used one of the largest open-access chest X-ray data sets named COVIDx containing three classes—COVID-19, normal, and pneumonia. For feature extraction, we have applied an effective CNN structure, namely EfficientNet, with ImageNet pre-training weights. The generated features are transferred into custom fine-tuned top layers followed by a set of model snapshots. The predictions of the model snapshots (which are created during a single training) are consolidated through two ensemble strategies, i.e., hard ensemble and soft ensemble, to enhance classification performance. In addition, a visualization technique is incorporated to highlight areas that distinguish classes, thereby enhancing the understanding of primal components related to COVID-19. The results of our empirical evaluations show that the proposed ECOVNet model outperforms the state-of-the-art approaches and significantly improves detection performance with 100% recall for COVID-19 and overall accuracy of 96.07%. We believe that ECOVNet can enhance the detection of COVID-19 disease, and thus, underpin a fully automated and efficacious COVID-19 detection system.

Download Full-text

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

Sensors ◽

10.3390/s22010283 ◽

2021 ◽

Vol 22 (1) ◽

pp. 283

Author(s):

Xiaoyuan Yu ◽

Suigu Tang ◽

Chak Fong Cheang ◽

Hon Ho Yu ◽

I Cheong Choi

Keyword(s):

Deep Learning ◽

Image Retrieval ◽

Learning Model ◽

Endoscopic Images ◽

Proposed Model ◽

Esophageal Lesion ◽

Lesion Analysis ◽

Diagnosis Accuracy ◽

Segmentation Task ◽

Deep Learning Model

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Download Full-text