Pneumonia Detection with Weighted Voting Ensemble of CNN Models

Ko, Heewon; Ha, Hyunsoo; Cho, Hyuna; Seo, Kiwon; Lee, Jihye

doi:10.1109/icaibd.2019.8837042

Cited by 25 publications

(4 citation statements)

References 10 publications

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“…Another research work explored the combination of RetinaNet and Mask R‐CNN for pneumonia detection 49 . They tried various ensembles of RetinaNet and Mask R‐CNN with different sizes and different weights.…”

Section: Methodsmentioning

confidence: 99%

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Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Shah

2022

Chronic Diseases and Translational Medicine

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Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

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Section: Methodsmentioning

confidence: 99%

“…Another research work explored the combination of RetinaNet and Mask R-CNN for pneumonia detection. 49 4).…”

Section: Localization Of Pneumonia In Chest X-raysmentioning

confidence: 99%

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Shah

2022

Chronic Diseases and Translational Medicine

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“…Another recent work involving a multi-level attention network with the Xception network as a backbone was developed, and the proposed model performed well in tumor classification [ 25 ]. Furthermore, ensemble learning generally achieves a better classification accuracy, which has been proven in previous medical imaging diagnosis tasks [ 37 , 38 , 39 ]. Therefore, in this work, the ability of pretrained and finetuned ViT models, both individually and in an ensemble manner, is evaluated for the classification of meningiomas, gliomas, and pituitary tumors from T1w CE MRI at both 224 × 224 and 384 × 384 resolutions, which, to the best of our knowledge, has not been implemented to date.…”

Section: Introductionmentioning

confidence: 94%

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

et al. 2022

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The automated classification of brain tumors plays an important role in supporting radiologists in decision making. Recently, vision transformer (ViT)-based deep neural network architectures have gained attention in the computer vision research domain owing to the tremendous success of transformer models in natural language processing. Hence, in this study, the ability of an ensemble of standard ViT models for the diagnosis of brain tumors from T1-weighted (T1w) magnetic resonance imaging (MRI) is investigated. Pretrained and finetuned ViT models (B/16, B/32, L/16, and L/32) on ImageNet were adopted for the classification task. A brain tumor dataset from figshare, consisting of 3064 T1w contrast-enhanced (CE) MRI slices with meningiomas, gliomas, and pituitary tumors, was used for the cross-validation and testing of the ensemble ViT model’s ability to perform a three-class classification task. The best individual model was L/32, with an overall test accuracy of 98.2% at 384 × 384 resolution. The ensemble of all four ViT models demonstrated an overall testing accuracy of 98.7% at the same resolution, outperforming individual model’s ability at both resolutions and their ensembling at 224 × 224 resolution. In conclusion, an ensemble of ViT models could be deployed for the computer-aided diagnosis of brain tumors based on T1w CE MRI, leading to radiologist relief.

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“…In [ 10 ], an ensemble of RetinaNet [ 11 ] and Mask RCNN models with ResNet-50 and ResNet-101 classifier backbones delivered a performance with a mAP of 0.2283 using the RSNA Kaggle pneumonia detection challenge CXR dataset. Another study [ 12 ] proposed a weighted-voting ensemble of the predictions from Mask R-CNN and RetinaNet models to achieve an mAP of 0.2174 in detecting pneumonia-consistent manifestations. These studies used the randomized test set split from the challenge-provided training data.…”

Section: Introductionmentioning

confidence: 99%

A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays

2022

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Pneumonia is an acute respiratory infectious disease caused by bacteria, fungi, or viruses. Fluid-filled lungs due to the disease result in painful breathing difficulties and reduced oxygen intake. Effective diagnosis is critical for appropriate and timely treatment and improving survival. Chest X-rays (CXRs) are routinely used to screen for the infection. Computer-aided detection methods using conventional deep learning (DL) models for identifying pneumonia-consistent manifestations in CXRs have demonstrated superiority over traditional machine learning approaches. However, their performance is still inadequate to aid in clinical decision-making. This study improves upon the state of the art as follows. Specifically, we train a DL classifier on large collections of CXR images to develop a CXR modality-specific model. Next, we use this model as the classifier backbone in the RetinaNet object detection network. We also initialize this backbone using random weights and ImageNet-pretrained weights. Finally, we construct an ensemble of the best-performing models resulting in improved detection of pneumonia-consistent findings. Experimental results demonstrate that an ensemble of the top-3 performing RetinaNet models outperformed individual models in terms of the mean average precision (mAP) metric (0.3272, 95% CI: (0.3006,0.3538)) toward this task, which is markedly higher than the state of the art (mAP: 0.2547). This performance improvement is attributed to the key modifications in initializing the weights of classifier backbones and constructing model ensembles to reduce prediction variance compared to individual constituent models.

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Pneumonia Detection with Weighted Voting Ensemble of CNN Models

Cited by 25 publications

References 10 publications

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Advancement of deep learning in pneumonia/Covid‐19 classification and localization: A systematic review with qualitative and quantitative analysis

Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling

A Deep Modality-Specific Ensemble for Improving Pneumonia Detection in Chest X-rays

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