Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks

Saraiva, Aratã Andrade; Santos, D. B. S.; Costa, Nator Junior C.; Sousa, José Vigno Moura; Ferreira, N. M. Fonseca; Valente, António; Soares, Salviano

doi:10.5220/0007346600760083

Cited by 54 publications

(30 citation statements)

References 15 publications

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“…They achieved an AUC score of 99.34. Saraiva et al [44], Ayan et al [45], and Rahman et al [46] used deep learning based methods and achieved an accuracy of 94.4%, 84.5%, and 98.0%, respectively. In all of these papers, the dataset used was of a similar size.…”

Section: Comparative Analysis Of Various Existing Methodsmentioning

confidence: 99%

“…A localization approach based on pre-trained DenseNet-121, along with feature extraction, was used to identify 14 thoracic diseases in [43]. Saraiva et al [44], Ayan et al [45], and Rahman et al [46] used deep learning based methods for pneumonia classification. Xiao et al [47] proposed a novel multi-scale heterogeneous three-dimensional (3D) convolutional neural network (MSH-CNN) based on chest computed tomography (CT) images.…”

Section: Related Workmentioning

confidence: 99%

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Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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Pneumonia causes the death of around 700,000 children every year and affects 7% of the global population. Chest X-rays are primarily used for the diagnosis of this disease. However, even for a trained radiologist, it is a challenging task to examine chest X-rays. There is a need to improve the diagnosis accuracy. In this work, an efficient model for the detection of pneumonia trained on digital chest X-ray images is proposed, which could aid the radiologists in their decision making process. A novel approach based on a weighted classifier is introduced, which combines the weighted predictions from the state-of-the-art deep learning models such as ResNet18, Xception, InceptionV3, DenseNet121, and MobileNetV3 in an optimal way. This approach is a supervised learning approach in which the network predicts the result based on the quality of the dataset used. Transfer learning is used to fine-tune the deep learning models to obtain higher training and validation accuracy. Partial data augmentation techniques are employed to increase the training dataset in a balanced way. The proposed weighted classifier is able to outperform all the individual models. Finally, the model is evaluated, not only in terms of test accuracy, but also in the AUC score. The final proposed weighted classifier model is able to achieve a test accuracy of 98.43% and an AUC score of 99.76 on the unseen data from the Guangzhou Women and Children’s Medical Center pneumonia dataset. Hence, the proposed model can be used for a quick diagnosis of pneumonia and can aid the radiologists in the diagnosis process.

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Section: Comparative Analysis Of Various Existing Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

et al. 2020

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“…Another approach discussed was to use DenseNet-121 that had been trained before using the extracted features to identify different fourteen thoracic diseases [48]. Some researchers, such as Saraiva et al [49], Ayan and Ünver [50], and Rahman et al [51], have applied deep learning techniques to classify types of pneumonia. Furthermore, researchers introduced a three-dimensional (3D) convolutional neural network (MSH-CNN) to develop a diverse multiscale novel that depends on CT images of the chest [52].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Deep Learning‐Based Pneumonia Detection Approach for Energy‐Efficient Medical Systems

ِAlmaslukh

2021

Wireless Communications and Mobile Computing

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Early detection of pneumonia disease can increase the survival rate of lung patients. Chest X-ray (CXR) images are the primarily means of detecting and diagnosing pneumonia. Detecting pneumonia from CXR images by a trained radiologist is a challenging task. It needs an automatic computer-aided diagnostic system to improve the accuracy of diagnosis. Developing a lightweight automatic pneumonia detection approach for energy-efficient medical systems plays an important role in improving the quality of healthcare with reduced costs and speedier response. Recent works have proposed to develop automated detection models using deep learning (DL) methods. However, the efficiency and effectiveness of these models need to be improved because they depend on the values of the models’ hyperparameters. Choosing suitable hyperparameter values is a critical task for constructing a lightweight and accurate model. In this paper, a lightweight DL approach is proposed using a pretrained DenseNet-121-based feature extraction method and a deep neural network- (DNN-) based method with a random search fine-tuning technique. The DenseNet-121 model is selected due to its ability to provide the best representation of lung features. The use of random search makes the tuning process faster and improves the efficiency and accuracy of the DNN model. An extensive set of experiments are conducted on a public dataset of CXR images using a set of evaluation metrics. The experiments show that the approach achieved 98.90% accuracy with an increase of 0.47% compared to the latest approach on the same dataset. Moreover, the experimental results demonstrate the approach that the average execution time for detection is very low, confirming its suitability for energy-efficient medical systems.

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“…They consist in multi-layer neural networks that recognize visual patterns from pixel images ( Shin et al, 2016 ). Among the different tools recently developed, the existing CNNs CXNet-m1 ( Xu et al, 2019 ), CheXNeXt ( Rajpurkar et al, 2018 ), VGG16 and VGG19 ( Toǧaçar et al, 2020 ), AlexNet ( Rahman et al, 2020 ; Rajaraman and Antani, 2020 ; Toǧaçar et al, 2020 ), ResNet18 ( Rahman et al, 2020 ; Rajaraman and Antani, 2020 ), DenseNet201 ( Rahman et al, 2020 ), SqueezeNet ( Rahman et al, 2020 ), VGGNet ( Rajaraman and Antani, 2020 ), GoogLeNet ( Saraiva et al, 2019 ), Lastly, Hashmi and collaborators proposed the most accurate and precise model regarding previous developed programs ( Hashmi et al, 2020 ) using ResNet18, Xception, InceptionV3, DenseNet121 and MobileNetV3 CNN algorithms. They could develop a robust model for bacterial pneumonia detection with the help of hospital-scale CXR and CT databases provided respectively from Wang et al (2017) (named ChestX-ray 14) and Kermany et al (2018) .…”

Section: Innovation In Diagnostics For Bacterial Pulmonary Infectionsmentioning

confidence: 99%

Which Current and Novel Diagnostic Avenues for Bacterial Respiratory Diseases?

et al. 2020

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Bacterial acute pneumonia is responsible for an extremely large burden of death worldwide and diagnosis is paramount in the management of patients. While multidrug-resistant bacteria is one of the biggest health threats in the coming decades, clinicians urgently need access to novel diagnostic technologies. In this review, we will first present the already existing and largely used techniques that allow identifying pathogen-associated pneumonia. Then, we will discuss the latest and most promising technological advances that are based on connected technologies (artificial intelligence-based and Omics-based) or rapid tests, to improve the management of lung infections caused by pathogenic bacteria. We also aim to highlight the mutual benefits of fundamental and clinical studies for a better understanding of lung infections and their more efficient diagnostic management.

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Models of Learning to Classify X-ray Images for the Detection of Pneumonia using Neural Networks

Cited by 54 publications

References 15 publications

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

Efficient Pneumonia Detection in Chest Xray Images Using Deep Transfer Learning

A Lightweight Deep Learning‐Based Pneumonia Detection Approach for Energy‐Efficient Medical Systems

Which Current and Novel Diagnostic Avenues for Bacterial Respiratory Diseases?

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