Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images

Mahmoudi, Sidi Ahmed; Stassin, Sédrick; Daho, Mostafa El Habib; Lessage, Xavier; Mahmoudi, Saïd

doi:10.1007/978-3-030-72752-9_16

Cited by 7 publications

(4 citation statements)

References 31 publications

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“…Using data augmentation methods, some enormous volumes of datasets must be tested using transfer learning or DL techniques [ 14 , 15 ]. Hence, this research model considers data augmentation methods with the datasets.…”

Section: Resultsmentioning

confidence: 99%

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Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Xue

Chinnaperumal²,

Abdulsahib

et al. 2023

Bioengineering

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Recently, various methods have been developed to identify COVID-19 cases, such as PCR testing and non-contact procedures such as chest X-rays and computed tomography (CT) scans. Deep learning (DL) and artificial intelligence (AI) are critical tools for early and accurate detection of COVID-19. This research explores the different DL techniques for identifying COVID-19 and pneumonia on medical CT and radiography images using ResNet152, VGG16, ResNet50, and DenseNet121. The ResNet framework uses CT scan images with accuracy and precision. This research automates optimum model architecture and training parameters. Transfer learning approaches are also employed to solve content gaps and shorten training duration. An upgraded VGG16 deep transfer learning architecture is applied to perform multi-class classification for X-ray imaging tasks. Enhanced VGG16 has been proven to recognize three types of radiographic images with 99% accuracy, typical for COVID-19 and pneumonia. The validity and performance metrics of the proposed model were validated using publicly available X-ray and CT scan data sets. The suggested model outperforms competing approaches in diagnosing COVID-19 and pneumonia. The primary outcomes of this research result in an average F-score (95%, 97%). In the event of healthy viral infections, this research is more efficient than existing methodologies for coronavirus detection. The created model is appropriate for recognition and classification pre-training. The suggested model outperforms traditional strategies for multi-class categorization of various illnesses.

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Section: Resultsmentioning

confidence: 99%

“…Using an SVM classification model, frequent patterns were generated from the pulmonary surface to distinguish between malignant and benign lung nodules. Backpropagation networks have been used to categorize imagery as normal or malignant using a gray-level co-occurrence matrix technique [ 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Xue

Chinnaperumal²,

Abdulsahib

et al. 2023

Bioengineering

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“…Figure 6 visualizes the mechanisms behind the XAI approaches reviewed in this section. Weight-based techniques utilize the product of final weights based on the connections from input neurons to the output neurons [72,124]. Gradient-based techniques backpropagate outputs onto a particular feature map (the output of one filter applied to the previous layer).…”

Section: Non-intrinsically Interpretable Modelsmentioning

confidence: 99%

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

Khalili,

Wimmer

2024

Life

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By applying AI techniques to a variety of pandemic-relevant data, artificial intelligence (AI) has substantially supported the control of the spread of the SARS-CoV-2 virus. Along with this, epidemiological machine learning studies of SARS-CoV-2 have been frequently published. While these models can be perceived as precise and policy-relevant to guide governments towards optimal containment policies, their black box nature can hamper building trust and relying confidently on the prescriptions proposed. This paper focuses on interpretable AI-based epidemiological models in the context of the recent SARS-CoV-2 pandemic. We systematically review existing studies, which jointly incorporate AI, SARS-CoV-2 epidemiology, and explainable AI approaches (XAI). First, we propose a conceptual framework by synthesizing the main methodological features of the existing AI pipelines of SARS-CoV-2. Upon the proposed conceptual framework and by analyzing the selected epidemiological studies, we reflect on current research gaps in epidemiological AI toolboxes and how to fill these gaps to generate enhanced policy support in the next potential pandemic.

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“…Moreover, explainability is applied using Grand-Cam visualization, and various existing GAN architectures are used to create realistic copied samples to cope with limited sample numbers. Mahmoudi et al [35] applied explainable DL models for the classification and segmentation of COVID-19 images by using different X-ray and CT scan images. Lo and Yin [36] introduced an interaction-based CNN model for COVID-19 detection using chest X-ray images.…”

Section: Covid19 Samplementioning

confidence: 99%

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

Khan

Azhar

Ibrar

et al. 2022

Computational Intelligence and Neuroscience

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COVID-19 detection and classification using chest X-ray images is a current hot research topic based on the important application known as medical image analysis. To halt the spread of COVID-19, it is critical to identify the infection as soon as possible. Due to time constraints and the expertise of radiologists, manually diagnosing this infection from chest X-ray images is a difficult and time-consuming process. Artificial intelligence techniques have had a significant impact on medical image analysis and have also introduced several techniques for COVID-19 diagnosis. Deep learning and explainable AI have shown significant popularity among AL techniques for COVID-19 detection and classification. In this work, we propose a deep learning and explainable AI technique for the diagnosis and classification of COVID-19 using chest X-ray images. Initially, a hybrid contrast enhancement technique is proposed and applied to the original images that are later utilized for the training of two modified deep learning models. The deep transfer learning concept is selected for the training of pretrained modified models that are later employed for feature extraction. Features of both deep models are fused using improved canonical correlation analysis that is further optimized using a hybrid algorithm named Whale-Elephant Herding. Through this algorithm, the best features are selected and classified using an extreme learning machine (ELM). Moreover, the modified deep models are utilized for Grad-CAM visualization. The experimental process was conducted on three publicly available datasets and achieved accuracies of 99.1, 98.2, and 96.7%, respectively. Moreover, the ablation study was performed and showed that the proposed accuracy is better than the other methods.

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Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images

Cited by 7 publications

References 31 publications

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets

Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic

COVID-19 Classification from Chest X-Ray Images: A Framework of Deep Explainable Artificial Intelligence

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