Automatic Detection of Pneumonia on Compressed Sensing Images using Deep Learning

Islam, Sheikh Rafiul; Maity, Santi P.; Ray, Ajoy Kumar; Mandal, Mrinal

doi:10.1109/ccece.2019.8861969

Cited by 30 publications

(23 citation statements)

References 10 publications

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“…Ref. [49] proposed a Compressed Sensing (CS) with a deep transfer learning model for automatic classification of pneumonia on X-ray images to assist the medical physicians. The dataset used for this work contained approximately 5850 X-ray data of two categories (abnormal /normal) obtained from Kaggle.…”

Section: Related Workmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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Section: Related Workmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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“…Furthermore, Li et al [42] used more than two CNNs (Convolution Neural Networks) to minimize the falsepositive rate in lung nodules of CXR images. Similarly, Islam et al [43] proposed an ensemble model, which was obtained by aggregating different pre-trained DL models to detect the abnormality in lung nodule of CXR images. Recently, Chouhan et al [22] proposed a model, which aggregates the outputs of five pre-trained models such as AlexNet, DenseNet-121, ResNet-18, Inception-V3, and GoogleNet, to detect pneumonia using the transfer learning approach on the CXR images.…”

Section: Combined Deep Learning-based Algorithmsmentioning

confidence: 99%

Attention-based VGG-16 model for COVID-19 chest X-ray image classification

2020

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Computer-aided diagnosis (CAD) methods such as Chest X-rays (CXR)-based method is one of the cheapest alternative options to diagnose the early stage of COVID-19 disease compared to other alternatives such as Polymerase Chain Reaction (PCR), Computed Tomography (CT) scan, and so on. To this end, there have been few works proposed to diagnose COVID-19 by using CXR-based methods. However, they have limited performance as they ignore the spatial relationships between the region of interests (ROIs) in CXR images, which could identify the likely regions of COVID-19’s effect in the human lungs. In this paper, we propose a novel attention-based deep learning model using the attention module with VGG-16. By using the attention module, we capture the spatial relationship between the ROIs in CXR images. In the meantime, by using an appropriate convolution layer (4th pooling layer) of the VGG-16 model in addition to the attention module, we design a novel deep learning model to perform fine-tuning in the classification process. To evaluate the performance of our method, we conduct extensive experiments by using three COVID-19 CXR image datasets. The experiment and analysis demonstrate the stable and promising performance of our proposed method compared to the state-of-the-art methods. The promising classification performance of our proposed method indicates that it is suitable for CXR image classification in COVID-19 diagnosis.

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“…The tool utilizes chest X-ray images to demonstrate the performance of identification. Islam et al [12] proposed a compressed sensing framework for automatic detection of pneumonia on X-ray images. This method had a prediction accuracy of 97.34% surpassing many of the current state-of-the-art methods.…”

Section: Related Studies a The Research Work Of Pneumonia Detectionmentioning

confidence: 99%

GeminiNet: Combine Fully Convolution Network With Structure of Receptive Fields for Object Detection

et al. 2020

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Pneumonia is a relatively common disease that will endanger the lives of patients if left untreated. End-to-end detection of pneumonia using neural networks will be helpful for reducing related workforce. CNN's processing of images shows remarkable performance, naturally, the use of CNN based methods for assisted reading will be a trend in modern medicine. The property of current detection algorithms is not yet satisfactory, so further research is extremely needed. In this article, we design GeminiNet to identify and localize the pneumonia in Chest X-ray (CXR) images. It uses a popular fully convolution architecture with computation shared on the entire image, combining RoI Align and PSRoI Pooling to capture global and local information and output. Our approach introduces DetNet59, a network designed specifically for detection to capture deep features. In the sixth stage of DetNet59, the structure of the retina-like convolutional layers is added to replace the fully connected layer. This structure uses the dilated convolution to extend the reconstructive field, and the convolution kernels of three different scales are used for parallel calculation to collect rich feature information. GeminiNet is validated on the RSNA dataset. We augment dataset by flipping on horizontal and vertical for the small amount of data. At IoU (Intersection over Union) = 0.5, AP reached 0.4575, 0.078 higher than ResNet50, and reached 0.7758 on the AUC indicator. GeminiNet achieves 8fps in detection speed, which is better than the 7fps of the popular Faster R-CNN architecture. INDEX TERMS Fully convolution network, DetNet59, receptive field block, GeminiNet, pneumonia detection.

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Automatic Detection of Pneumonia on Compressed Sensing Images using Deep Learning

Cited by 30 publications

References 10 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Attention-based VGG-16 model for COVID-19 chest X-ray image classification

GeminiNet: Combine Fully Convolution Network With Structure of Receptive Fields for Object Detection

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