Classiﬁcation of Images of Childhood Pneumonia using Convolutional Neural Networks

Saraiva, Aratã Andrade; Ferreira, Nuno M. F.; Sousa, Luciano Lopes de; Costa, Nator Junior C.; Sousa, José Vigno Moura; Santos, D. B. S.; Valente, António; Soares, Salviano

doi:10.5220/0007404301120119

Cited by 79 publications

(57 citation statements)

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“…In terms of classi cation between Non-COVID-19 viral pneumonia and Healthy CXR images, several studies utilized same dataset made available by Kermany et al, 2018 [42]. Majority of these studies achieved higher performance of above 90% Accuracy such as Stephen et al 2019 [36], Saravia et al, 2019 [38] and Rajaraman et al, 2018 [40]. However, our model achieved result within same range with 94.43% Accuracy.…”

Section: Comparison Between Our Results With State Of Artmentioning

confidence: 71%

Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Özsöz

Ibrahim

Serte

et al. 2020

Preprint

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Background: The outbreak of COVID-19 on the eve of January 2020 has led to global crisis around the world. The disease was declared pandemic by World Health Organization (WHO) in mid-March. Currently the outbreak has affected more than 150 countries with more than 20 million confirmed cases and more than 700,000 death tolls. The standard method for detection of COVID-19 is the Reverse-Transcription Polymerase Chain Reaction (RT-PCR) which is less sensitive, expensive and required specialized health expert. As the number of cases continue to grow, there is high need for developing rapid screening method that is accurate, fast and cheap. Methods: We proposed the use of Deep Learning approach based on Pretrained AlexNet Model for classification of COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia and normal Chest X-rays Images (CXR) scans obtained from different public databases. Result and Conclusion: For non-COVID-19 viral pneumonia and healthy datasets, the model achieved 94.43% accuracy, 98.19% Sensitivity and 95.78% Specificity. For bacterial pneumonia and healthy datasets, the model achieved 91.43% accuracy, 91.94% sensitivity and 100% Specificity. For COVID-19 pneumonia and healthy CXR images, the model achieved 99.16% accuracy, 97.44% sensitivity and 100% Specificity. For classification of COVID-19 pneumonia and non-COVID-19 viral pneumonia, the model achieved 99.62% accuracy, 90.63% sensitivity and 99.89% Specificity. For multiclass datasets the model achieved 94.00% accuracy, 91.30% sensitivity and 84.78% specificity for COVID-19, bacterial pneumonia and healthy. For 4 classes (COVID-19, non-COVID-19 viral pneumonia, bacterial pneumonia and healthy, the model achieved accuracy of 93.42%, sensitivity of 89.18% and specificity of 98.92%.

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Section: Comparison Between Our Results With State Of Artmentioning

confidence: 71%

Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Özsöz

Ibrahim

Serte

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…In another similar research, Stephen et al [51] illustrated an efficient deep learning approach for pneumonia classification, by using four convolutional layers and two dense layers in addition to classical image augmentation and achieved 93.73% testing accuracy. Later, Saraiva et al [44] experimented convolutional neural networks to classify images of childhood pneumonia by using a deep learning model with seven convolutional layers along with three dense layers while achieving 95.30% testing accuracy. Liang and Zheng [27] demonstrated a transfer learning method with a deep residual network for pediatric pneumonia diagnosis with 49 convolutional layers and two dense layers and achieved 96.70% testing accuracy.…”

Section: Related Workmentioning

confidence: 99%

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

2020

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The novel coronavirus 2019 (COVID-19) is a respiratory syndrome that resembles pneumonia. The current diagnostic procedure of COVID-19 follows reverse-transcriptase polymerase chain reaction (RT-PCR) based approach which however is less sensitive to identify the virus at the initial stage. Hence, a more robust and alternate diagnosis technique is desirable. Recently, with the release of publicly available datasets of corona positive patients comprising of computed tomography (CT) and chest X-ray (CXR) imaging; scientists, researchers and healthcare experts are contributing for faster and automated diagnosis of COVID-19 by identifying pulmonary infections using deep learning approaches to achieve better cure and treatment. These datasets have limited samples concerned with the positive COVID-19 cases, which raise the challenge for unbiased learning. Following from this context, this article presents the random oversampling and weighted class loss function approach for unbiased fine-tuned learning (transfer learning) in various state-of-the-art deep learning approaches such as baseline ResNet, Inception-v3, Inception ResNet-v2, DenseNet169, and NASNetLarge to perform binary classification (as normal and COVID-19 cases) and also multi-class classification (as COVID-19, pneumonia, and normal case) of posteroanterior CXR images. Accuracy, precision, recall, loss, and area under the curve (AUC) are utilized to evaluate the performance of the models. Considering the experimental results, the performance of each model is scenario dependent; however, NASNetLarge displayed better scores in contrast to other architectures, which is further compared with other recently proposed approaches. This article also added the visual explanation to illustrate the basis of model classification and perception of COVID-19 in CXR images.

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“…Optimasi pada CNN dapat membantu meningkatkan akurasi, salah satunya adalah adaptive moments atau adam (Kingma & Ba, 2014). Saraiva et al (2019) membahas tentang klasifikasi penyakit pneumonia pada anakanak dengan menggunakan convolutional neural network. Penelitian tersebut menggunakan validasi silang k-fold untuk mengevaluasi kapasitas generalisasi model dan bertujuan untuk meningkatkan akurasi dengan validasi silang k-fold dengan akurasi rata-rata sebesar 95,30%.…”

Section: Pendahuluanunclassified

Klasifikasi Penyakit Pneumonia Menggunakan Metode Convolutional Neural Network Dengan Optimasi Adaptive Momentum

Andika

Pratiwi

Handajani

2019

IJSA

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Pneumonia is an infection of the bacterium Streptococcus pneumoniae which causes inflammation in the air bag in one or both lungs. Pneumonia is a disease that can spread through the patient's air splashes. Pneumonia can be dangerous because it can cause death, therefore it is necessary to have early detection using chest radiograph images to determine the symptoms of pneumonia. Diagnosis using a chest radiograph image manually by medical personnel or a doctor requires a long time, even difficult to detect pneumonia disase. Convolutional neural network (CNN) is a deep learning method that adopts the performance of human brain neurons called neural network and convolution functions to classify images. CNN can also help classify pneumonia based on chest radiograph images. This study used data from Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification as many as 5860 images entered into two classes, namely normal and pneumonia, then 2400 data samples were taken using simple random sampling. This study uses adaptive momentum optimization (Adam) which serves to improve the accuracy of the model. Adam optimization is a development of existing optimizations such as Stochastic gradient descent (SGD), AdaGard, and RMSProp. The classification results of the models built were 99.98% for training data with 100 epochs, and accuracy in the test data was 78% which means that the model was able to qualify 78% of the test data into normal classes and pneumonia appropriately.

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Classiﬁcation of Images of Childhood Pneumonia using Convolutional Neural Networks

Cited by 79 publications

References 0 publications

Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Viral and Bacterial Pneumonia Detection using Artificial Intelligence in the Era of COVID-19

Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks

Klasifikasi Penyakit Pneumonia Menggunakan Metode Convolutional Neural Network Dengan Optimasi Adaptive Momentum

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