Automated Detection of Multiple Lesions on Chest X-ray Images: Classification Using a Neural Network Technique with Association-Specific Contexts

Automated detection of lung lesions on Chest X-ray images shows good performance to reduce lung cancer mortality. However, it is difficult to detect multiple lesions of single image well and truly, and additional efforts are needed to improve diagnostic efficiency and quality. In this paper, a multi-label classification model combining attention-based neural networks and association-specific contexts is proposed for the detection of multiple lesions on chest X-ray images. A convolutional neural network and a long short-term memory network are first aligned by an attention mechanism to take advantage of both image and text information for the detection, called CNN-ATTENTION-LSTM (CAL) network. In addition, a mining method of implicit association strength to obtain an association network of chest lesions (CLA) network is designed to guide the training of CAL network. The CLA network provides possible clinical relationships between lesions to help the CAL network obtain better predictions. Experimental results on ChestX-ray14 dataset show that our method outperforms some state-of-the-art models under the metrics of area under curve (AUC), precision, recall, and F-score and achieves up to 85.4% in the case of atelectasis and infiltration. It indicates that the method may be useful in the computer-aided detection of multiple lesions on chest X-ray images.

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Automated Detection of COVID-19 Pneumonia and Non COVID-19 Pneumonia from Chest X-ray Images Using Convolutional Neural Network (CNN)

10.1109/icitech50181.2021.9590174 ◽

2021 ◽

Author(s):

Nazmus Shakib Shadin ◽

Silvia Sanjana ◽

Mayisha Farzana

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Automated Detection ◽

X Ray ◽

Chest X Ray

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One-shot Cluster-Based Approach for the Detection of COVID–19 from Chest X–ray Images

Cognitive Computation ◽

10.1007/s12559-020-09774-w ◽

2021 ◽

Cited By ~ 2

Author(s):

V. N. Manjunath Aradhya ◽

Mufti Mahmud ◽

D. S. Guru ◽

Basant Agarwal ◽

M. Shamim Kaiser

Keyword(s):

Neural Network ◽

Machine Learning ◽

Probabilistic Neural Network ◽

Classification Model ◽

Superior Performance ◽

Generalized Regression Neural Network ◽

X Rays ◽

X Ray ◽

Proposed Model ◽

Chest X Ray

AbstractCoronavirus disease (COVID-19) has infected over more than 28.3 million people around the globe and killed 913K people worldwide as on 11 September 2020. With this pandemic, to combat the spreading of COVID-19, effective testing methodologies and immediate medical treatments are much required. Chest X-rays are the widely available modalities for immediate diagnosis of COVID-19. Hence, automation of detection of COVID-19 from chest X-ray images using machine learning approaches is of greater demand. A model for detecting COVID-19 from chest X-ray images is proposed in this paper. A novel concept of cluster-based one-shot learning is introduced in this work. The introduced concept has an advantage of learning from a few samples against learning from many samples in case of deep leaning architectures. The proposed model is a multi-class classification model as it classifies images of four classes, viz., pneumonia bacterial, pneumonia virus, normal, and COVID-19. The proposed model is based on ensemble of Generalized Regression Neural Network (GRNN) and Probabilistic Neural Network (PNN) classifiers at decision level. The effectiveness of the proposed model has been demonstrated through extensive experimentation on a publicly available dataset consisting of 306 images. The proposed cluster-based one-shot learning has been found to be more effective on GRNN and PNN ensembled model to distinguish COVID-19 images from that of the other three classes. It has also been experimentally observed that the model has a superior performance over contemporary deep learning architectures. The concept of one-shot cluster-based learning is being first of its kind in literature, expected to open up several new dimensions in the field of machine learning which require further researching for various applications.

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A CNN Classification Model For Diagnosis Covid19

10.20944/preprints202007.0591.v1 ◽

2020 ◽

Author(s):

Ahmed Abdullah Farid ◽

hatem khater ◽

gamal selim

Keyword(s):

Neural Network ◽

Virus Disease ◽

Real Data ◽

Classification Model ◽

X Ray ◽

Corona Virus ◽

Chest X Ray ◽

Neural Network Structure ◽

Extraction Model ◽

First Time

The paper demonstrates the analysis of Corona Virus Disease based on a CNN probabilistic model. It involves a technique for classification and prediction by recognizing typical and diagnostically most important CT images features relating to Corona Virus. The main contributions of the research include predicting the probability of recurrences in no recurrence (first time detection) cases at applying our proposed Convolution neural network structure. The Study is validated on 2002 chest X-ray images with 60 confirmed positive covid19 cases and (650 bacterial – 412 viral -880 normal) x-ray images. The proposed CNN compared with traditional classifiers with proposed CHFS feature extraction model. The experimental study has done with real data demonstrates the feasibility and potential of the proposed approach for the said cause. The result of proposed CNN structure has been successfully done to achieve 98.20% accuracy of covid19 potential cases with comparable of traditional classifiers.

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Performance Result for Detection of COVID-19 using Deep Learning

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.g5684.059720 ◽

2020 ◽

Vol 9 (7) ◽

pp. 699-703

Keyword(s):

Neural Network ◽

Classification Model ◽

Classification Models ◽

X Ray ◽

The World ◽

Rectified Linear Unit ◽

Chest X Ray ◽

Novel Coronavirus ◽

The Masses ◽

Fully Connected

The 2019 novel coronavirus (COVID-19), which has sprawled fleetly among masses residing in distant nations, had a prefatory juncture in China. From both a safeness and a lucrative outlook, it has staggered the world with its hasty diffusion with conjectural vicious generic repercussions for the masses. Consequent to the escalating cases daily, there is a constricted fraction of COVID-19 inspection kits acquirable in healthcare institutions. Ergo, to obviate COVID-19 propagating betwixt masses, it is imperative to enforce an instinctive unveiling network as a prompt jack legging diagnosis appendage. The contemplated method embroils a convolutional neural network- based model, namely ResNet50, concerted with a Fully Connected Layer (FCL), reinforced by Rectified Linear Unit (ReLU) for the unearthing of coronavirus pneumonia imparted sufferer by harnessing chest X-ray radiographs. The endorsed classification model, i.e. resnet50 affirmed by FCL and ReLU, compassed accuracy of 94% for unearthing COVID-19. When equated to diverse classification models, the purported model is preeminent. The aftereffect is premised on the attested X-ray images from the data appropriable in the arsenal of Kaggle

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