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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 November 2023) | Viewed by 16579

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Special Issue Editors

Dr. Jose-Maria Buades-Rubio

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Guest Editor

Computer Graphics and Vision and AI Group (UGiVIA), Research Institute of Health Sciences (IUNICS), Department of Mathematics and Computer Science, Universitat de les Illes Balears, 07122 Palma, Spain
Interests: computer graphics; computer vision; artificial intelligence

Dr. Antoni Jaume-i-Capó

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Guest Editor

Computer Graphics and Vision and AI Group (UGiVIA), Research Institute of Health Sciences (IUNICS), Laboratori d’Aplicacions de la Intel·ligència Artificial de la UIB (LAIA@UIB), Department of Mathematics and Computer Science, Universitat de les Illes Balears, 07122 Palma, Spain
Interests: computer vision; vision-based interfaces; interactive systems for motor rehabilitation; medical image processing; human-based computing for image processing

Special Issue Information

Dear Colleagues,

Artificial intelligence is poised to change the world in the coming decades, from the way we do business to domestic applications. Artificial intelligence gives systems the ability to learn and make decisions, benefiting areas such as medicine and well-being.

This Special Issue aims to disseminate the latest research results and developments in artificial intelligence for health and well-being. We at inviting researchers and practitioners to contribute their high-quality original research or review articles on these topics to this Special Issue.

Topics of interest include, but are not limited to:

Machine learning in health and well-being;
Data analysis in health and well-being;
Social robots;
Affective computing;
Medical image processing;
Intelligent medical devices and sensors;
Medical expert systems;
Explaining AI in medical systems;
Computer-aided diagnosis.

Dr. Jose-Maria Buades-Rubio
Dr. Antoni Jaume-i-Capó
Guest Editors

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Keywords

artificial intelligence
machine learning
healthcare and well-being informatics
explainable systems
deep learning
bioinformatics
AI in medicine

Published Papers (10 papers)

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Research

20 pages, 1500 KiB

Open AccessArticle

A New Method for 2D-Adapted Wavelet Construction: An Application in Mass-Type Anomalies Localization in Mammographic Images

by Damian Valdés-Santiago, Angela M. León-Mecías, Marta Lourdes Baguer Díaz-Romañach, Antoni Jaume-i-Capó, Manuel González-Hidalgo and Jose Maria Buades Rubio

Appl. Sci. 2024, 14(1), 468; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010468 - 04 Jan 2024

Viewed by 817

Abstract

This contribution presents a wavelet-based algorithm to detect patterns in images. A two-dimensional extension of the DST-II is introduced to construct adapted wavelets using the equation of the tensor product corresponding to the diagonal coefficients in the 2D discrete wavelet transform. A 1D filter was then estimated that meets finite energy conditions, vanished moments, orthogonality, and four new detection conditions. These allow, when performing the 2D transform, for the filter to detect the pattern by taking the diagonal coefficients with values of the normalized similarity measure, defined by Guido, as greater than 0.7, and

α = 0.1

. The positions of these coefficients are used to estimate the position of the pattern in the original image. This strategy has been used successfully to detect artificial patterns and localize mass-like abnormalities in digital mammography images. In the case of the latter, high sensitivity and positive predictive value in detection were achieved but not high specificity or negative predictive value, contrary to what occurred in the 1D strategy. This means that the proposed detection algorithm presents a high number of false negatives, which can be explained by the complexity of detection in these types of images. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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15 pages, 4365 KiB

Open AccessArticle

Method of Improving the Management of Cancer Risk Groups by Coupling a Features-Attention Mechanism to a Deep Neural Network

by Darian M. Onchis, Flavia Costi, Codruta Istin, Ciprian Cosmin Secasan and Gabriel V. Cozma

Appl. Sci. 2024, 14(1), 447; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010447 - 04 Jan 2024

Viewed by 802

Abstract

(1) Background: Lung cancers are the most common cancers worldwide, and prostate cancers are among the second in terms of the frequency of cancers diagnosed in men. Automatic ranking of the risk groups of such diseases is highly in demand, but the clinical practice has shown us that, for a sensitive screening of the clinical parameters using an artificial intelligence system, a customarily defined deep neural network classifier is not sufficient given the usually small size of medical datasets. (2) Methods: In this paper, we propose a new management method of cancer risk groups based on a supervised neural network model that is further enhanced by using a features attention mechanism in order to boost its level of accuracy. For the analysis of each clinical parameter, we used local interpretable model-agnostic explanations, which is a post hoc model-agnostic technique that outlines feature importance. After that, we applied the feature-attention mechanism in order to obtain a higher weight after training. We tested the method on two datasets, one for binary-class in cases of thoracic cancer and one for multi-class classification in cases of urological cancer, to demonstrate the wide availability and versatility of the method. (3) Results: The accuracy levels of the models trained in this way reached values of more than 80% for both clinical tasks. (4) Conclusions: Our experiments demonstrate that, by using explainability results as feedback signals in conjunction with the attention mechanism, we were able to increase the accuracy of the base model by more than 20% on small medical datasets, reaching a critical threshold for providing recommendations based on the collected clinical parameters. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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17 pages, 4564 KiB

Open AccessArticle

A Multi-Task Learning and Multi-Branch Network for DR and DME Joint Grading

by Xiaoxue Xing, Shenbo Mao, Minghan Yan, He Yu, Dongfang Yuan, Cancan Zhu, Cong Zhang, Jian Zhou and Tingfa Xu

Appl. Sci. 2024, 14(1), 138; https://0-doi-org.brum.beds.ac.uk/10.3390/app14010138 - 22 Dec 2023

Viewed by 515

Abstract

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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23 pages, 9777 KiB

Open AccessArticle

A Novel Dynamic Approach for Determining Real-Time Interior Visual Comfort Exploiting Machine Learning Techniques

by Christos Tzouvaras, Asimina Dimara, Alexios Papaioannou, Christos-Nikolaos Anagnostopoulos, Stelios Krinidis, Konstantinos Arvanitis, Dimosthenis Ioannidis and Dimitrios Tzovaras

Appl. Sci. 2023, 13(12), 6975; https://0-doi-org.brum.beds.ac.uk/10.3390/app13126975 - 09 Jun 2023

Viewed by 1274

Abstract

The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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18 pages, 1953 KiB

Open AccessArticle

Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis

by Arshad Hashmi and Omar Barukab

Appl. Sci. 2023, 13(3), 1464; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031464 - 22 Jan 2023

Cited by 5 | Viewed by 2336

Abstract

Neurodegeneration and impaired neuronal transmission in the brain are at the root of Alzheimer’s disease (AD) and dementia. As of yet, no successful treatments for dementia or Alzheimer’s disease have indeed been found. Therefore, preventative measures such as early diagnosis are essential. This research aimed to evaluate the accuracy of the Open Access Series of Imaging Studies (OASIS) database for the purpose of identifying biomarkers of dementia using effective machine learning methods. In most parts of the world, AD is responsible for dementia. When the challenge level is high, it is nearly impossible to get anything done without assistance. This is increasing due to population growth and the diagnostic period. Two current approaches are the medical history and testing. The main challenge for dementia research is the imbalance of datasets and their impact on accuracy. A proposed system based on reinforcement learning and neural networks could generate and segment imbalanced classes. Making a precise diagnosis and taking into account dementia in all four stages will result in high-resolution sickness probability maps. It employs deep reinforcement learning to generate accurate and understandable representations of a person’s dementia sickness risk. To avoid an imbalance, classes should be evenly represented in the samples. There is a significant class imbalance in the MRI image. The Deep Reinforcement System improved trial accuracy by 6%, precision by 9%, recall by 13%, and F-score by 9–10%. The diagnosis efficiency has improved as well. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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13 pages, 2879 KiB

Open AccessArticle

Application of Speech on Stress Recognition with Neural Network in Nuclear Power Plant

by Jiaqi Chen, Bohan Wu, Kaijie Xie, Shu Ma, Zhen Yang and Yi Shen

Appl. Sci. 2023, 13(2), 779; https://0-doi-org.brum.beds.ac.uk/10.3390/app13020779 - 05 Jan 2023

Cited by 3 | Viewed by 1214

Abstract

Human failures occur in nuclear power plants when operators are under acute stress. Therefore, an automatic stressed recognition system should be developed for nuclear power work. Previous studies on the prediction of stress are limited because of their reliance on subjective ratings and contact physiological measurement. To solve this problem, we developed a non-intrusive way by using voice features to detect stress. We aim to build a system that can estimate the level of stress from speech which may be applied to nuclear power plants where operators engage in regular verbal communication as part of their duties. In this study, we collected voice recordings from 34 participants during a simulated nuclear plant power task in a time-limited situation that requires high cognitive resources. Mel frequency cepstrum coefficients (MFCCs) were extracted from stressed voice samples and the neural network model was used to assess stress levels continuously. The experimental results showed that voice features can provide satisfactory predictions of the stress state. Mean relative errors of prediction are possible within approximately 5%. We discuss the implications of the use of voice as a minimally intrusive means for monitoring the effects of stress on cognitive performance in practical applications. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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22 pages, 23506 KiB

Open AccessArticle

Multi-Objective Deep Reinforcement Learning for Personalized Dose Optimization Based on Multi-Indicator Experience Replay

by Lin Huo and Yuepeng Tang

Appl. Sci. 2023, 13(1), 325; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010325 - 27 Dec 2022

Cited by 7 | Viewed by 2813

Abstract

Chemotherapy as an effective method is now widely used to treat various types of malignant tumors. With advances in medicine and drug dosimetry, the precise dose adjustment of chemotherapy drugs has become a significant challenge. Several academics have investigated this problem in depth. However, these studies have concentrated on the efficiency of cancer treatment while ignoring other significant bodily indicators in the patient, which could cause other complications. Therefore, to handle the above problem, this research creatively proposes a multi-objective deep reinforcement learning. First, in order to balance the competing indications inside the optimization process and to give each indicator a better outcome, we propose a multi-criteria decision-making strategy based on the integration concept. In addition, we provide a novel multi-indicator experience replay for multi-objective deep reinforcement learning, which significantly speeds up learning compared to conventional approaches. By modeling various indications in the body of the patient, our approach is used to simulate the treatment of tumors. The experimental results demonstrate that the treatment plan generated by our method can better balance the contradiction between the tumor’s treatment effect and other biochemical indicators than other treatment plans, and its treatment time is only one-third that of multi-objective deep reinforcement learning, which is now in use. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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15 pages, 464 KiB

Open AccessArticle

Reducing Operation Costs of Thyroid Nodules Using Machine Learning Algorithms with Thyroid Nodules Scoring Systems

by Erdal Ayvaz, Kaplan Kaplan, Fatma Kuncan, Ednan Ayvaz and Hüseyin Türkoğlu

Appl. Sci. 2022, 12(22), 11559; https://0-doi-org.brum.beds.ac.uk/10.3390/app122211559 - 14 Nov 2022

Viewed by 1277

Abstract

Continuous advancement in the health sector is essential to reduce costs and increase efficiency and quality of service. The widespread use of ultrasonography (USG) has made it possible to detect thyroid nodules with higher success rates. Some standard scoring systems have been developed to score thyroid nodules. Thyroid scoring systems are classification systems that determine the risk of cancer in thyroid nodules according to ultrasonographic characteristics and nodule size. Different scoring results for the same thyroid nodule may occur according to these different scoring systems, which can cause some unnecessary surgical interventions. In this study, some intelligent models are developed to assist thyroid scoring systems, with the aim to determine the correct surgical intervention and reduce operation costs by preventing unnecessary interventions and surgical procedures. The integration of current thyroid scoring systems (K-TIRADS, ACR-TIRADS, EU-TIRADS, ATA, and BTA) and machine learning methods provides radiologists and clinicians a decision-support mechanism in the evaluation of thyroid nodules. Correct diagnosis will help to reduce costs by helping prevent unnecessary procedures. The present dataset was retrospectively constructed using ultrasound images of thyroid nodules between 2014 and 2018. In determining the treatment process of thyroid nodules, Random Forest, Adaboost, J48 Decision Tree (J48 DT), and Support Vector Machine (SVM) models are used for increased prediction accuracy of thyroid scoring systems. The goal is to decrease redundant Fine Needle Aspiration (FNA) biopsies and surgical interventions of suspicious thyroid nodules. As a result of the study, higher degrees of accuracy are achieved in the determination of correct or incorrect surgical interventions of thyroid nodules using the J48 DT algorithm with the EU-TIRADS scoring system, with an accuracy rate of 99.7853%, compared to other classifiers. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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14 pages, 833 KiB

Open AccessArticle

Brain Tumor Classification Using Conditional Segmentation with Residual Network and Attention Approach by Extreme Gradient Boost

by Arshad Hashmi and Ahmed Hamza Osman

Appl. Sci. 2022, 12(21), 10791; https://0-doi-org.brum.beds.ac.uk/10.3390/app122110791 - 25 Oct 2022

Cited by 8 | Viewed by 1388

Abstract

A brain tumor is a tumor in the brain that has grown out of control, which is a dangerous condition for the human body. For later prognosis and treatment planning, the accurate segmentation and categorization of cancers are crucial. Radiologists must use an automated approach to identify brain tumors, since it is an error-prone and time-consuming operation. This work proposes conditional deep learning for brain tumor segmentation, residual network-based classification, and overall survival prediction using structural multimodal magnetic resonance images (MRI). First, we propose conditional random field and convolution network-based segmentation, which identifies non-overlapped patches. These patches need minimal time to identify the tumor. If they overlap, the errors increase. The second part of this paper proposes residual network-based feature mapping with XG-Boost-based learning. In the second part, the main emphasis is on feature mapping in nonlinear space with residual features, since residual features reduce the chances of loss information, and nonlinear space mapping provides efficient tumor information. Features mapping learned by XG-Boost improves the structural-based learning and increases the accuracy class-wise. The experiment uses two datasets: one for two classes (cancer and non-cancer) and the other for three classes (meningioma, glioma, pituitary). The performance on both improves significantly compared to another existing approach. The main objective of this research work is to improve segmentation and its impact on classification performance parameters. It improves by conditional random field and residual network. As a result, two-class accuracy improves by 3.4% and three-class accuracy improves by 2.3%. It is enhanced with a small convolution network. So, we conclude in fewer resources, and better segmentation improves the results of brain tumor classification. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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23 pages, 2172 KiB

Open AccessArticle

Application of Deep Learning Techniques and Bayesian Optimization with Tree Parzen Estimator in the Classification of Supply Chain Pricing Datasets of Health Medications

by David Opeoluwa Oyewola, Emmanuel Gbenga Dada, Temidayo Oluwatosin Omotehinwa, Onyeka Emebo and Olugbenga Oluseun Oluwagbemi

Appl. Sci. 2022, 12(19), 10166; https://0-doi-org.brum.beds.ac.uk/10.3390/app121910166 - 10 Oct 2022

Cited by 8 | Viewed by 3046

Abstract

From the development and sale of a product through its delivery to the end customer, the supply chain encompasses a network of suppliers, transporters, warehouses, distribution centers, shipping lines, and logistics service providers all working together. Lead times, bottlenecks, cash flow, data management, risk exposure, traceability, conformity, quality assurance, flaws, and language barriers are some of the difficulties that supply chain management faces. In this paper, deep learning techniques such as Long Short-Term Memory (LSTM) and One Dimensional Convolutional Neural Network (1D-CNN) were adopted and applied to classify supply chain pricing datasets of health medications. Then, Bayesian optimization using the tree parzen estimator and All K Nearest Neighbor (AllkNN) was used to establish the suitable model hyper-parameters of both LSTM and 1D-CNN to enhance the classification model. Repeated five-fold cross-validation is applied to the developed models to predict the accuracy of the models. The study showed that the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) outperforms other approaches employed in this study. The accuracy of the combination of 1D-CNN, AllkNN, and Bayesian optimization (1D-CNN+AllKNN+BO) from one-fold to 10-fold, produced the highest range between 61.2836% and 63.3267%, among other models. Full article

(This article belongs to the Special Issue Artificial Intelligence for Health and Well-Being)

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