Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

Sobahi, Nebras; Atila, Orhan; Deniz, Erkan; Şengür, Abdulkadir; Acharya, U. Rajendra

doi:10.1016/j.bbe.2022.08.005

Cited by 16 publications

(5 citation statements)

References 34 publications

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“…While the above metrics are quantitative, qualitative assessment is essential to gauge user satisfaction and understanding. Surveys and questionnaires can gather feedback from users regarding clarity, satisfaction, and perceived accuracy of explanations 97 . Further to this “User Task Performance” can evaluate how well users perform specific tasks using the explanations, such as model debugging or feature engineering.…”

Section: Evaluating Explainable Artificial Intelligence Methodsmentioning

confidence: 99%

“…Surveys and questionnaires can gather feedback from users regarding clarity, satisfaction, and perceived accuracy of explanations. 97 Further to this "User Task Performance" can evaluate how well users perform specific tasks using the explanations, such as model debugging or feature engineering. Also, there are challenges in evaluating XAI.…”

Section: Dimensions Of Explainable Artificial Intelligence Evaluationsmentioning

confidence: 99%

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Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Raghavan,

Balasubramanian,

Veezhinathan

2024

Computational Intelligence

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There is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad‐CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.

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Section: Evaluating Explainable Artificial Intelligence Methodsmentioning

confidence: 99%

Section: Dimensions Of Explainable Artificial Intelligence Evaluationsmentioning

confidence: 99%

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Raghavan,

Balasubramanian,

Veezhinathan

2024

Computational Intelligence

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“…SMOTE oversampling was utilized to balance the training set. In [ 10 ], a fractal-dimension (FD) image is proposed for COVID-19 cough detection. The Coughvid, Virufy, and Coswara datasets were used in this study and reported 98.45%, 98.15%, and 97.59% accuracy, respectively.…”

Section: Introductionmentioning

confidence: 99%

Accumulated bispectral image-based respiratory sound signal classification using deep learning

Sangle¹,

Gaikwad²

2023

SIViP

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The COVID-19 virus is increasingly crucial to human health since new variants appear frequently. Detection of COVID-19 through respiratory sound has been an important area of research. This study analyzes respiratory sounds using novel accumulated bi-spectral features. The principal domain bispectrum is used for computing accumulated bispectrum. The resulting magnitude bispectrum is used in forming the bispectral image. In this work, a convolutional neural network (CNN) and ResNet-50 algorithms are designed to classify respiratory sounds as either COVID-19 or healthy. The performance of the proposed method is compared with the state-of-the-art methods. The proposed CNN-based method achieves the highest accuracy of 87.68% for shallow breath sounds, and ResNet-50 achieves the highest accuracy of 87.62% for deep breath sounds. Similarly, proposed methods gives the improved performance for other respiratory sounds.

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“…In addition, artificial intelligence systems can be successfully applied in a wide range of fields from lip-reading applications [8] to document language [9] and gesture recognition [10], epileptic seizures [11] and heart disease detection [12]. Similarly, artificial intelligence systems based on cough sounds [13,14] and especially chest images (X-Ray and CT scan) are widely used in COVID-19 diagnosis [15,16].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

ÖZAYDIN

Tekin

2023

Turkish Journal of Science and Technology

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COVID-19, which has been declared a pandemic disease, has affected the lives of millions of people and caused a major epidemic. Despite the development of vaccines and vaccination to prevent the transmission of the disease, COVID-19 case rates fluctuate worldwide. Therefore, rapid and reliable diagnosis of COVID-19 disease is of critical importance. For this purpose, a hybrid model based on transfer learning methods and ensemble classifiers is proposed in this study. In this hybrid approach, called DeepFeat-E, the diagnosis process is performed by using deep features obtained from transfer learning models and ensemble classifiers consisting of classical machine learning methods. To test the proposed approach, a dataset of 21,165 X-ray images including 10,192 Normal, 6012 Lung Opacity, 1345 Viral Pneumonia and 3616 COVID-19 were used. With the proposed approach, the highest accuracy was achieved with the deep features of the DenseNet201 transfer learning model and the Stacking ensemble learning method. Accordingly, the test accuracy was 90.17%, 94.99% and 94.93% for four, three and two class applications, respectively. According to the results obtained in this study, it is seen that the proposed hybrid system can be used quickly and reliably in the diagnosis of COVID-19 and lower respiratory tract infections.

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Explainable COVID-19 detection using fractal dimension and vision transformer with Grad-CAM on cough sounds

Cited by 16 publications

References 34 publications

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

Accumulated bispectral image-based respiratory sound signal classification using deep learning

A Hybrid Model Based on Deep Features and Ensemble Learning for the Diagnosis of COVID-19: DeepFeat-E

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