The effect of machine learning explanations on user trust for automated diagnosis of COVID-19

Goel, Kanika; Sindhgatta, Renuka; Kalra, Sumit; Goel, Rohan; Mutreja, Preeti

doi:10.1016/j.compbiomed.2022.105587

Cited by 24 publications

(12 citation statements)

References 29 publications

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“…However, recent publications have suggested that this gradient averaging step may limit the accuracy of the visual explanations by blurring the effects of individual spatial locations within feature maps, sometimes leading to lower-resolution interpretations and enhancing irrelevant regions. [43][44][45][46] Fig. 3 CNN explainability method for tumour Raman spectra: (1) a sample spectrum is passed through the trained CNN to obtain the class scores, y c , and the feature maps of the last convolutional layer, A k .…”

Section: Grad-cam Technique For Model Interpretabilitymentioning

confidence: 99%

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network

Fuentes,

Milligan,

Wiebe

et al. 2024

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Section: Grad-cam Technique For Model Interpretabilitymentioning

confidence: 99%

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network

Fuentes,

Milligan,

Wiebe

et al. 2024

Analyst

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“…Deep learning-based neural networks are widely used in industrial artifcial intelligence and have rapidly promoted the development of computer image recognition, autonomous driving, and computer-aided medical diagnosis. Furthermore, with the discovery of function approximators for multilayer feedback neural networks, deep learning methods have also been used to solve various partial differential equations (PDEs) in the feld of mathematics [1][2][3][4][5][6][7][8][9][10][11][12]. Zhu and Zabaras solved PDEs with deep convolutional neural networks, which are commonly applied in image regression analysis [13].…”

Section: Introductionmentioning

confidence: 99%

An Improved Physics-Informed Neural Network Algorithm for Predicting the Phreatic Line of Seepage

Gao

Yao

et al. 2023

Advances in Civil Engineering

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As new ways to solve partial differential equations (PDEs), physics-informed neural network (PINN) algorithms have received widespread attention and have been applied in many fields of study. However, the standard PINN framework lacks sufficient seepage head data, and the method is difficult to apply effectively in seepage analysis with complex boundary conditions. In addition, the differential type Neumann boundary makes the solution more difficult. This study proposed an improved prediction method based on a PINN with the aim of calculating PDEs with complex boundary conditions such as Neumann boundary conditions, in which the spatial distribution characteristic information is increased by a small amount of measured data and the loss equation is dynamically adjusted by loss weighting coefficients. The measured data are converted into a quadratic regular term and added to the loss function as feature data to guide the update process for the weight and bias coefficient of each neuron in the neural network. A typical geotechnical problem concerning seepage phreatic line determination in a rectangular dam is analyzed to demonstrate the efficiency of the improved method. Compared with the standard PINN algorithm, due to the addition of measurement data and dynamic loss weighting coefficients, the improved PINN algorithm has better convergence and can handle more complex boundary conditions. The results show that the improved method makes it convenient to predict the phreatic line in seepage analysis for geotechnical engineering projects with measured data.

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“…The medical field has already leveraged artificial neural network (ANN) techniques with machine learning and deep learning algorithms to assist radiologists in detecting and diagnosing SARS-CoV2 based on lung CT imaging techniques [19][20][21][22][23][24]. Specifically, convolutional neural network (CNN)based deep learning algorithms are preferred to address imaging classification for COVID-19 diagnosis [25] due to the specialized feature extraction capabilities for digital images.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

Sameer

Mutlag

Gharghan

2023

IET Image Processing

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The global economy has been dramatically impacted by COVID‐19, which has spread to be a pandemic. COVID‐19 virus affects the respiratory system, causing difficulty breathing in the patient. It is crucial to identify and treat infections as soon as possible. Traditional diagnostic reverse transcription‐polymerase chain reaction (RT‐PCR) methods require more time to find the infection. A high infection rate, slow laboratory analysis, and delayed test results caused the widespread and uncontrolled spread of the disease. This study aims to diagnose the COVID‐19 epidemic by leveraging a modified convolutional neural network (CNN) to quickly and safely predict the disease's appearance from computed tomography (CT) scan images and a laboratory and physiological parameters dataset. A dataset representing 500 patients was used to train, test, and validate the CNN model with results in detecting COVID‐19 having an accuracy, sensitivity, specificity, and F1‐score of 99.33%, 99.09%, 99.52%, and 99.24%, respectively. These experimental results suggest that our strategy performs better than previously published approaches.

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The effect of machine learning explanations on user trust for automated diagnosis of COVID-19

Cited by 24 publications

References 29 publications

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network

An Improved Physics-Informed Neural Network Algorithm for Predicting the Phreatic Line of Seepage

Deep learning‐based COVID‐19 diagnosis using CT scans with laboratory and physiological parameters

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