Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Alqudah, Ali Mohammad; Qazan, Shoroq; Masad, Ihssan S.

doi:10.21203/rs.3.rs-66836/v2

Cited by 5 publications

(6 citation statements)

References 14 publications

(18 reference statements)

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“…It is clear from the table that PneumoniaNet outperforms all other models with respect to all important performance metrics. The new model accuracy is the highest, with 99.72% accuracy, a 5% increase from the second-best model accuracy of 94.03% reported by Alqudah et al in 2021 [16]. Because the accuracy metric by itself can be misleading, other performance metrics of interest such as the new model sensitivity, specificity, precision, and AUC are reported, and they are also better than what is described in the literature.…”

Section: Discussionmentioning

confidence: 58%

“…Sensitivity is the most important metric in medical applications because it shows the percentage of correct positives, and Table 3 shows that the proposed model recall is 99.74%. It is also clear in Table 3 that all previous work [13][14][15][16][17] achieved lower accuracy, sensitivity, precision, and F1 when compared with the suggested method. On top of that, their dataset size is smaller than the one used in this paper.…”

Section: Discussionmentioning

confidence: 86%

“…Previous research [13,14,17] focused on using transfer learning and pretrained models, although only two groups [15,16] attempted to build a model from scratch or to modify an existing model for the purpose of detecting pneumonia as this study did. Most researchers [13][14][15] used the GWCMC dataset that this research used; nevertheless, Rahman et al [14] used a Kaggle dataset, and Madhubala et al [17] used images from Mendeley.…”

Section: Discussionmentioning

confidence: 99%

“…In 2021, Alqudah et al [16] used a modified CNN framework to distinguish bacterial and viral pneumonia from normal CXRs in 5852 images. The framework consisted of two stages; firstly, a CNN was used for feature extraction, and, in the later stage, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers were utilized.…”

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

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PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

et al. 2021

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Pneumonia is an inflammation of the lung parenchyma that is caused by a variety of infectious microorganisms and non-infective agents. All age groups can be affected; however, in most cases, fragile groups are more susceptible than others. Radiological images such as Chest X-ray (CXR) images provide early detection and prompt action, where typical CXR for such a disease is characterized by radiopaque appearance or seemingly solid segment at the affected parts of the lung due to inflammatory exudate formation replacing the air in the alveoli. The early and accurate detection of pneumonia is crucial to avoid fatal ramifications, particularly in children and seniors. In this paper, we propose a novel 50 layers Convolutional Neural Network (CNN)-based architecture that outperforms the state-of-the-art models. The suggested framework is trained using 5852 CXR images and statistically tested using five-fold cross-validation. The model can distinguish between three classes: viz viral, bacterial, and normal; with 99.7% ± 0.2 accuracy, 99.74% ± 0.1 sensitivity, and 0.9812 Area Under the Curve (AUC). The results are promising, and the new architecture can be used to recognize pneumonia early with cost-effectiveness and high accuracy, especially in remote areas that lack proper access to expert radiologists, and therefore, reduces pneumonia-caused mortality rates.

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Section: Discussionmentioning

confidence: 58%

Section: Discussionmentioning

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Background and State-of-the-art Researchmentioning

confidence: 99%

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PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

et al. 2021

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“…Pre-trained networks are trained on an Imagenet database [ 40 ] consisting of 1000 image classes. Even though trained on non-biomedical images, pre-trained CNNs in combination with off-the-shelf classifiers were found successful in the detection of a wide range of diseases from X-ray images, including tuberculosis [ 41 ], breast cancer [ 42 ] and pneumonia [ 43 ]. The convolutional layers built on top of each other, learn more complex features for reliable classification tasks.…”

Section: Methodsmentioning

confidence: 99%

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network

Gayathri¹,

Abraham²,

Sujarani³

et al. 2022

Computers in Biology and Medicine

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Several infectious diseases have affected the lives of many people and have caused great dilemmas all over the world. COVID-19 was declared a pandemic caused by a newly discovered virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) by the World Health Organisation in 2019. RT-PCR is considered the golden standard for COVID-19 detection. Due to the limited RT-PCR resources, early diagnosis of the disease has become a challenge. Radiographic images such as Ultrasound, CT scans, X-rays can be used for the detection of the deathly disease. Developing deep learning models using radiographic images for detecting COVID-19 can assist in countering the outbreak of the virus. This paper presents a computer-aided detection model utilizing chest X-ray images for combating the pandemic. Several pre-trained networks and their combinations have been used for developing the model. The method uses features extracted from pre-trained networks along with Sparse autoencoder for dimensionality reduction and a Feed Forward Neural Network (FFNN) for the detection of COVID-19. Two publicly available chest X-ray image datasets, consisting of 504 COVID-19 images and 542 non-COVID-19 images, have been combined to train the model. The method was able to achieve an accuracy of 0.957 8 and an AUC of 0.982 1, using the combination of InceptionResnetV2 and Xception. Experiments have proved that the accuracy of the model improves with the usage of sparse autoencoder as the dimensionality reduction technique.

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A lightweight hybrid deep learning system for cardiac valvular disease classification

Al-Issa

Alqudah

2022

Sci Rep

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Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.

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Artificial Intelligence Framework for Efficient Detection and Classification of Pneumonia Using Chest Radiography Images

Cited by 5 publications

References 14 publications

PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

PneumoniaNet: Automated Detection and Classification of Pediatric Pneumonia Using Chest X-ray Images and CNN Approach

A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network

A lightweight hybrid deep learning system for cardiac valvular disease classification

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