Explainable-by-Design Approach for Covid-19 Classification via Ct-Scan

Soares, Eduardo; Angelov, Plamen; Biaso, Sarah; Fróes, Michele Higa; Abe, Daniel Kanda

doi:10.1101/2020.04.24.20078584

Cited by 149 publications

(99 citation statements)

References 21 publications

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“…Table 2 presents the numerical results obtained using the proposed semi-supervised shallow neural network model, ResNet50 [9], 3D-UNet [10], Kang et al [15] and Wang et al [18] for COVID-19 detection on the Brazilian data Figure 6: PQIS-Net segmented lung CT slice#171 [30] with the three different masks. set [29]. The standard evaluation metrics used in Table 2 to measure the COVID-19 detection efficiency are accuracy, precision, recall, F1-score and AUC (Area under ROC curve) [31].…”

Section: Resultsmentioning

confidence: 99%

“…In addition to this segmentation, experiments are also set up for classifications the proposed Semi-supervised model, ResNet50 [9], 3D-UNet [10]. The other state of the art techniques include Kang et al [15] and Wang et al [18] for COVID-19 detection on the Brazilian data set [29]. The evaluation process involves the manually segmented lesion mask as ground truth and each 2D pixel is predicted as either True Positive (T RP ) or True Negative (T RN ) or False Positive (T RN ) or False Negative (F LN ).…”

Section: Methodsmentioning

confidence: 99%

“…(a − h) PQIS-Net segmented lung CT slices#[29] with Pneumonia (Non-Covid) infection followed by (i − p) color mapping for class level L = 8 with activation ξ (a) Epochs#5(b) Epochs#10…”

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confidence: 99%

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Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Konar

Panigrahi

Bhattacharyya

et al. 2020

Preprint

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Infection of Novel Coronavirus 2019 (COVID-19) on lung cells and human respiratory systems have raised real concern to the human lives during the current pandemic spread across the world. Recent observations on CT images of human lungs infected by COVID-19 is a challenging task for the researchers in finding suitable image patterns for automatic diagnosis. In this paper, a novel semi-supervised shallow learning network model comprising Parallel Quantum-Inspired Self-supervised Network (PQIS-Net) with Fully Connected (FC) layers is proposed for automatic segmentation followed by patch-based classifications on segmented lung CT images for the diagnosis of COVID-19 disease. The PQIS-Net model is incorporated for fully automated segmentation of lung CT scan images obviating pre-trained convolutional neural network models for feature learning. The PQIS-Net model comprises a trinity of layered structures of quantum bits inter-connected through rotation gates using an 8-connected second-order neighborhood topology for the segmentation of wide variation of local intensities of the CT images. Intensive experiments have been carried out on two publicly available lung CT image data sets thereby achieving promising segmentation outcome and diagnosis efficiency (F1-score and AUC) while compared with the state of the art pre-trained convolutional based models.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Konar

Panigrahi

Bhattacharyya

et al. 2020

Preprint

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“…The main bottleneck for the realization of a study such as the ones cited above is the lack of good quality comprehensive data sets. Possibly the rst attempt to create such a data set was the so-called COVID-CT dataset [15] which consists of images mined from research papers. Different versions of this dataset were used in [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

“…The remainder of this work is organized as follows. Section 2 present the de-tails of COVID-CT [15] and SARS-CoV-2 CT-scan [14] datasets. The method-ology is described in Section 3 and the experiments along with the results in Section 4.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

Luz

Silva

Moreira

et al. 2020

Preprint

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Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. To achieve these goals, in this work, we propose a slice voting-based approach extending the EfficientNet Family of deep artificial neural networks.We also design a specific data augmentation process and transfer learning for such task.Moreover, a cross-dataset study is performed into the two largest datasets to date. The proposed method presents comparable results to the state-of-the-art methods and the highest accuracy to date on both datasets (accuracy of 87.60\% for the COVID-CT dataset and accuracy of 98.99% for the SARS-CoV-2 CT-scan dataset). The cross-dataset analysis showed that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario.These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

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DML‐OFA: Deep mutual learning with online feature alignment for the detection of COVID‐19 from chest x‐ray images

Liang,

Lu,

Ming

et al. 2024

Concurrency and Computation

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SummaryCOVID‐19 is a novel coronavirus‐induced disease and automatic identification of COVID‐19 using computer‐assisted methods can facilitate faster diagnostic efficiency. Current research typically employs a single model for COVID‐19 identification, while implicit and complementary knowledge between heterogeneous networks is neglected. To address these issues, we propose a new model based on deep mutual learning with online feature alignment called DML‐OFA to more effectively diagnose COVID‐19. First, we use a traditional deep mutual learning (DML) framework to allow two parallel heterogeneous networks to learn from each other to form two effective feature extractors. In addition, we embed the adaptive feature fusion classifier and logits ensembling module in the proposed DML‐OFA, which can simultaneously learn implicit complementary knowledge from feature maps and logits. We evaluated DML‐OFA on four public datasets: Covid‐chestxray‐dataset, ChestXRay2017, Coronavirus‐dataset and COVIDx. The results showed that our model attains 97.10 Accuracy, 97.28 Specificity, 96.21 Recall, 97.45 Precision, and 96.82 F1‐score, which outperforms other previous related works.

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Explainable-by-Design Approach for Covid-19 Classification via Ct-Scan

Cited by 149 publications

References 21 publications

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Auto-diagnosis of COVID-19 using Lung CT Images with Semi-supervised Shallow Learning Network

Efficient Deep Learning Model for COVID-19 Detection in large CT images datasets: A cross-dataset analysis

DML‐OFA: Deep mutual learning with online feature alignment for the detection of COVID‐19 from chest x‐ray images

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