Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes

Oda, Masahiro; Zheng, Tong; Hayashi, Yuichiro; Otake, Yoshito; Hashimoto, Masahiro; Akashi, Toshiaki; Aoki, Shigeki; Mori, Kensaku

doi:10.1117/12.2613317

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…The baseline model is a 2D-3D hybrid CNN proposed by Oda et al 3 combined with a 3D attention module, which we extend from a 2D attention module. 4 The input of this model is a volume V and the outputs are likelihoods y (0) and y (1) of typical and non-typical COVID-19, respectively.…”

Section: Baseline Modelmentioning

confidence: 99%

Performance improvement for medical image classification model by using gradient-based analytical feature selection

Toda,

Itoh,

Oda

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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This paper presents a gradient-based analytical method for improving medical image classification. The automated classification of diseases is important in computer-aided diagnosis. In addition to accurate classification, its explainability is an essential factor toward its practical application. A gradient-based visual explanation provides the explainability of a model of an convolutional neural network (CNN) by indicating important patterns in an input image. Most studies use this explanation to assess CNN's validity in a qualitative manner. On the other hand, in addition to a model's explainability, our motivation is to utilize the visual-explanation methods to enhance the classification accuracy of a CNN model. In this study, we propose a weight-analysis-based method to improve the classification accuracy of a trained-CNN model without additional training. The proposed method selects important patterns based on a gradient-based weight analysis of a middle layer in a trained model and suppresses irrelevant patterns in the extracted features for the classification. We applied our analytical method to a convolutional and a global-average-pooling layers in a CNN, which classifies a chest CT volume into COVID-19 typical and non-typical cases. As shown in classification results on 302 testing cases, our method improved the accuracy of the COVID-19 classification.

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Section: Baseline Modelmentioning

confidence: 99%

Performance improvement for medical image classification model by using gradient-based analytical feature selection

Toda,

Itoh,

Oda

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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show abstract

“…Ensemble techniques aim to improve the robustness and accuracy of CNNs [75][76][77]. Ensemble methods for CNN models include mixture ensemble of CNNs [78] used for breast tumor classification [79], ensembles of pre-trained CNNs (such as inception v3) [80] used for epilepsy classification [78], in-network ensembles for obstructive sleep apnea detection [81,82], weighted average ensembles for pneumonia detection [83], a self-ensemble framework [84] used for brain lesion segmentation, orthogonal and attentive ensemble networks [85] used for COVID-19 diagnosis [86,87], 3D CNN ensembles used for pulmonary nodule classification in lung cancer screening [88] and ensembles of REFINED-CNN built under different choices of distance metrics and/or projection schemes used for anti-cancer drug sensitivity prediction [89]. Ensembled designs consisting of deep CNN and recurrent neural network architecture are applied for the recognition of end-to-end arousal from ECG signals [90].…”

Section: Ensemble Approaches For Cnn Modelsmentioning

confidence: 99%

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Mohammed,

Tune,

Assefa

et al. 2024

MAKE

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In this review, we compiled convolutional neural network (CNN) methods which have the potential to automate the manual, costly and error-prone processing of medical images. We attempted to provide a thorough survey of improved architectures, popular frameworks, activation functions, ensemble techniques, hyperparameter optimizations, performance metrics, relevant datasets and data preprocessing strategies that can be used to design robust CNN models. We also used machine learning algorithms for the statistical modeling of the current literature to uncover latent topics, method gaps, prevalent themes and potential future advancements. The statistical modeling results indicate a temporal shift in favor of improved CNN designs, such as a shift from the use of a CNN architecture to a CNN-transformer hybrid. The insights from statistical modeling point that the surge of CNN practitioners into the medical imaging field, partly driven by the COVID-19 challenge, catalyzed the use of CNN methods for detecting and diagnosing pathological conditions. This phenomenon likely contributed to the sharp increase in the number of publications on the use of CNNs for medical imaging, both during and after the pandemic. Overall, the existing literature has certain gaps in scope with respect to the design and optimization of CNN architectures and methods specifically for medical imaging. Additionally, there is a lack of post hoc explainability of CNN models and slow progress in adopting CNNs for low-resource medical imaging. This review ends with a list of open research questions that have been identified through statistical modeling and recommendations that can potentially help set up more robust, improved and reproducible CNN experiments for medical imaging.

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“…A classification model was proposed in the prior work. 5 The model has a 2D/3D hybrid feature extraction system. The first half 2D part is divided into three subparts for axial, coronal, and sagittal plane analyses.…”

Section: Classification Schemementioning

confidence: 99%

“…The purpose of this paper is to present an improved method for the COVID-19 classification. We combine a complex-architecture CNN, which specializes in feature extraction from anisotropic chest CT volumes, 5 with an ensemble learning method considering the inter-model diversity named orthogonal ensemble networks (OEN). 6 This strategy is based on the model's feature extraction ability could be boosted by its ensemble with various extracted features.…”

Section: Introductionmentioning

confidence: 99%

Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks

Toda

Oda

Hayashi

et al. 2023

Medical Imaging 2023: Computer-Aided Diagnosis

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This paper introduces the improved method for the COVID-19 classification based on computed tomography (CT) volumes using a combination of a complex-architecture convolutional neural network (CNN) and orthogonal ensemble networks (OEN). The novel coronavirus disease reported in 2019 (COVID-19) is still spreading worldwide. Early and accurate diagnosis of COVID-19 is required in such a situation, and the CT scan is an essential examination. Various computer-aided diagnosis (CAD) methods have been developed to assist and accelerate doctors' diagnoses. Although one of the effective methods is ensemble learning, existing methods combine some major models which do not specialize in COVID-19. In this study, we attempted to improve the performance of a CNN for the COVID-19 classification based on chest CT volumes. The CNN model specializes in feature extraction from anisotropic chest CT volumes. We adopt the OEN, an ensemble learning method considering inter-model diversity, to boost its feature extraction ability. For the experiment, We used chest CT volumes of 1283 cases acquired in multiple medical institutions in Japan. The classification result on 257 test cases indicated that the combination could improve the classification performance.

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Automated classification method of COVID-19 cases from chest CT volumes using 2D and 3D hybrid CNN for anisotropic volumes

Cited by 3 publications

References 8 publications

Performance improvement for medical image classification model by using gradient-based analytical feature selection

Performance improvement for medical image classification model by using gradient-based analytical feature selection

Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature

Improved method for COVID-19 classification of complex-architecture CNN from chest CT volumes using orthogonal ensemble networks

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