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Explainable AI in Medical Sensors

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (28 February 2023) | Viewed by 25542

Special Issue Editors


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Guest Editor
Informatics Building School of Informatics, University of Leicester, Leicester LE1 7RH, UK
Interests: artificial intelligence; deep learning; medical image processingrecognition; transfer learning; medical image analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA
Interests: biomedical data representation; data mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There exist many biomedical sensors, such as ultrasound sensors, chemical analysis sensors, biomaterial sensors, fluid flow sensors, MRI sensors, etc., in current medical research. These medical sensors are being developed with the help of advanced signal processing techniques. Meanwhile, artificial intelligence (AI) has gained recognition for its success in processing sensor data. Most AI models present impressively predictive accuracies, but they are recognized as “black boxes”. This proposal aims to provide diverse but complementary contributions to demonstrate the new developments and applications for explainable AI in processing medical sensor data.

Dr. Yu-Dong Zhang
Prof. Dr. Juan Manuel Gorriz
Prof. Dr. Yuankai Huo
Guest Editors

Manuscript Submission Information

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Keywords

  • machine learning
  • deep learning
  • medical sensor
  • explainable artificial intelligence
  • medical image analysis
  • graph neural network
  • big data
  • imaging sensors

Published Papers (7 papers)

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Research

14 pages, 3293 KiB  
Article
Endoscopic Image Classification Based on Explainable Deep Learning
by Doniyorjon Mukhtorov, Madinakhon Rakhmonova, Shakhnoza Muksimova and Young-Im Cho
Sensors 2023, 23(6), 3176; https://doi.org/10.3390/s23063176 - 16 Mar 2023
Cited by 9 | Viewed by 3517
Abstract
Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model [...] Read more.
Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model decisions are often made without reason or explanation. To reduce this gap, explainable artificial intelligence (XAI) offers a huge opportunity to receive informed decision support from deep learning models and opens the black box of the method. We conducted an explainable deep learning method based on ResNet152 combined with Grad–CAM for endoscopy image classification. We used an open-source KVASIR dataset that consisted of a total of 8000 wireless capsule images. The heat map of the classification results and an efficient augmentation method achieved a high positive result with 98.28% training and 93.46% validation accuracy in terms of medical image classification. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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30 pages, 3243 KiB  
Article
Explainable Artificial Intelligence (XAI) in Pain Research: Understanding the Role of Electrodermal Activity for Automated Pain Recognition
by Philip Gouverneur, Frédéric Li, Kimiaki Shirahama, Luisa Luebke, Wacław M. Adamczyk, Tibor M. Szikszay, Kerstin Luedtke and Marcin Grzegorzek
Sensors 2023, 23(4), 1959; https://doi.org/10.3390/s23041959 - 09 Feb 2023
Cited by 4 | Viewed by 2081
Abstract
Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. [...] Read more.
Artificial intelligence and especially deep learning methods have achieved outstanding results for various applications in the past few years. Pain recognition is one of them, as various models have been proposed to replace the previous gold standard with an automated and objective assessment. While the accuracy of such models could be increased incrementally, the understandability and transparency of these systems have not been the main focus of the research community thus far. Thus, in this work, several outcomes and insights of explainable artificial intelligence applied to the electrodermal activity sensor data of the PainMonit and BioVid Heat Pain Database are presented. For this purpose, the importance of hand-crafted features is evaluated using recursive feature elimination based on impurity scores in Random Forest (RF) models. Additionally, Gradient-weighted class activation mapping is applied to highlight the most impactful features learned by deep learning models. Our studies highlight the following insights: (1) Very simple hand-crafted features can yield comparative performances to deep learning models for pain recognition, especially when properly selected with recursive feature elimination. Thus, the use of complex neural networks should be questioned in pain recognition, especially considering their computational costs; and (2) both traditional feature engineering and deep feature learning approaches rely on simple characteristics of the input time-series data to make their decision in the context of automated pain recognition. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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18 pages, 5173 KiB  
Article
An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization
by Marriam Nawaz, Tahira Nazir, Ali Javed, Usman Tariq, Hwan-Seung Yong, Muhammad Attique Khan and Jaehyuk Cha
Sensors 2022, 22(2), 434; https://doi.org/10.3390/s22020434 - 07 Jan 2022
Cited by 58 | Viewed by 5367
Abstract
Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of [...] Read more.
Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate screening procedures are dependent on the availability of human experts who performs the manual analysis of retinal samples to identify the glaucomatous-affected regions. However, due to complex glaucoma screening procedures and shortage of human resources, we often face delays which can increase the vision loss ratio around the globe. To cope with the challenges of manual systems, there is an urgent demand for designing an effective automated framework that can accurately identify the Optic Disc (OD) and Optic Cup (OC) lesions at the earliest stage. Efficient and effective identification and classification of glaucomatous regions is a complicated job due to the wide variations in the mass, shade, orientation, and shapes of lesions. Furthermore, the extensive similarity between the lesion and eye color further complicates the classification process. To overcome the aforementioned challenges, we have presented a Deep Learning (DL)-based approach namely EfficientDet-D0 with EfficientNet-B0 as the backbone. The presented framework comprises three steps for glaucoma localization and classification. Initially, the deep features from the suspected samples are computed with the EfficientNet-B0 feature extractor. Then, the Bi-directional Feature Pyramid Network (BiFPN) module of EfficientDet-D0 takes the computed features from the EfficientNet-B0 and performs the top-down and bottom-up keypoints fusion several times. In the last step, the resultant localized area containing glaucoma lesion with associated class is predicted. We have confirmed the robustness of our work by evaluating it on a challenging dataset namely an online retinal fundus image database for glaucoma analysis (ORIGA). Furthermore, we have performed cross-dataset validation on the High-Resolution Fundus (HRF), and Retinal Image database for Optic Nerve Evaluation (RIM ONE DL) datasets to show the generalization ability of our work. Both the numeric and visual evaluations confirm that EfficientDet-D0 outperforms the newest frameworks and is more proficient in glaucoma classification. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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28 pages, 15494 KiB  
Article
Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features
by Anis Malekzadeh, Assef Zare, Mahdi Yaghoobi, Hamid-Reza Kobravi and Roohallah Alizadehsani
Sensors 2021, 21(22), 7710; https://doi.org/10.3390/s21227710 - 19 Nov 2021
Cited by 34 | Viewed by 4328
Abstract
Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists [...] Read more.
Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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13 pages, 3197 KiB  
Article
Using Explainable Machine Learning to Improve Intensive Care Unit Alarm Systems
by José A. González-Nóvoa, Laura Busto, Juan J. Rodríguez-Andina, José Fariña, Marta Segura, Vanesa Gómez, Dolores Vila and César Veiga
Sensors 2021, 21(21), 7125; https://doi.org/10.3390/s21217125 - 27 Oct 2021
Cited by 15 | Viewed by 3395
Abstract
Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis [...] Read more.
Due to the continuous monitoring process of critical patients, Intensive Care Units (ICU) generate large amounts of data, which are difficult for healthcare personnel to analyze manually, especially in overloaded situations such as those present during the COVID-19 pandemic. Therefore, the automatic analysis of these data has many practical applications in patient monitoring, including the optimization of alarm systems for alerting healthcare personnel. In this paper, explainable machine learning techniques are used for this purpose, with a methodology based on age-stratification, boosting classifiers, and Shapley Additive Explanations (SHAP) proposed. The methodology is evaluated using MIMIC-III, an ICU patient research database. The results show that the proposed model can predict mortality within the ICU with AUROC values of 0.961, 0.936, 0.898, and 0.883 for age groups 18–45, 45–65, 65–85 and 85+, respectively. By using SHAP, the features with the highest impact in predicting mortality for different age groups and the threshold from which the value of a clinical feature has a negative impact on the patient’s health can be identified. This allows ICU alarms to be improved by identifying the most important variables to be sensed and the threshold values at which the health personnel must be warned. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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15 pages, 3851 KiB  
Article
Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions
by Kashif Nisar, Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Ag Asri Ag Ibrahim, Joel J. P. C. Rodrigues, Samy Refahy Mahmoud, Bhawani Shankar Chowdhry and Manoj Gupta
Sensors 2021, 21(19), 6498; https://doi.org/10.3390/s21196498 - 29 Sep 2021
Cited by 7 | Viewed by 2020
Abstract
The aim of this work is to solve the case study singular model involving the Neumann–Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search [...] Read more.
The aim of this work is to solve the case study singular model involving the Neumann–Robin, Dirichlet, and Neumann boundary conditions using a novel computing framework that is based on the artificial neural network (ANN), global search genetic algorithm (GA), and local search sequential quadratic programming method (SQPM), i.e., ANN-GA-SQPM. The inspiration to present this numerical framework comes through the objective of introducing a reliable structure that associates the operative ANNs features using the optimization procedures of soft computing to deal with such stimulating systems. Four different problems that are based on the singular equations involving Neumann–Robin, Dirichlet, and Neumann boundary conditions have been occupied to scrutinize the robustness, stability, and proficiency of the designed ANN-GA-SQPM. The proposed results through ANN-GA-SQPM have been compared with the exact results to check the efficiency of the scheme through the statistical performances for taking fifty independent trials. Moreover, the study of the neuron analysis based on three and 15 neurons is also performed to check the authenticity of the proposed ANN-GA-SQPM. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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34 pages, 10819 KiB  
Article
Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems
by Ali Akbar Kekha Javan, Mahboobeh Jafari, Afshin Shoeibi, Assef Zare, Marjane Khodatars, Navid Ghassemi, Roohallah Alizadehsani and Juan Manuel Gorriz
Sensors 2021, 21(11), 3925; https://doi.org/10.3390/s21113925 - 07 Jun 2021
Cited by 27 | Viewed by 2945
Abstract
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly [...] Read more.
In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application. Full article
(This article belongs to the Special Issue Explainable AI in Medical Sensors)
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