1
|
Mishra A, Tabassum N, Aggarwal A, Kim YM, Khan F. Artificial Intelligence-Driven Analysis of Antimicrobial-Resistant and Biofilm-Forming Pathogens on Biotic and Abiotic Surfaces. Antibiotics (Basel) 2024; 13:788. [PMID: 39200087 PMCID: PMC11351874 DOI: 10.3390/antibiotics13080788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2024] [Revised: 08/14/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
The growing threat of antimicrobial-resistant (AMR) pathogens to human health worldwide emphasizes the need for more effective infection control strategies. Bacterial and fungal biofilms pose a major challenge in treating AMR pathogen infections. Biofilms are formed by pathogenic microbes encased in extracellular polymeric substances to confer protection from antimicrobials and the host immune system. Biofilms also promote the growth of antibiotic-resistant mutants and latent persister cells and thus complicate therapeutic approaches. Biofilms are ubiquitous and cause serious health risks due to their ability to colonize various surfaces, including human tissues, medical devices, and food-processing equipment. Detection and characterization of biofilms are crucial for prompt intervention and infection control. To this end, traditional approaches are often effective, yet they fail to identify the microbial species inside biofilms. Recent advances in artificial intelligence (AI) have provided new avenues to improve biofilm identification. Machine-learning algorithms and image-processing techniques have shown promise for the accurate and efficient detection of biofilm-forming microorganisms on biotic and abiotic surfaces. These advancements have the potential to transform biofilm research and clinical practice by allowing faster diagnosis and more tailored therapy. This comprehensive review focuses on the application of AI techniques for the identification of biofilm-forming pathogens in various industries, including healthcare, food safety, and agriculture. The review discusses the existing approaches, challenges, and potential applications of AI in biofilm research, with a particular focus on the role of AI in improving diagnostic capacities and guiding preventative actions. The synthesis of the current knowledge and future directions, as described in this review, will guide future research and development efforts in combating biofilm-associated infections.
Collapse
Affiliation(s)
- Akanksha Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India;
| | - Nazia Tabassum
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Ashish Aggarwal
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India;
| | - Young-Mog Kim
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Department of Food Science and Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Fazlurrahman Khan
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Institute of Fisheries Science, Pukyong National University, Busan 48513, Republic of Korea
- International Graduate Program of Fisheries Science, Pukyong National University, Busan 48513, Republic of Korea
| |
Collapse
|
2
|
Aldughayfiq B, Ashfaq F, Jhanjhi NZ, Humayun M. A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG. Diagnostics (Basel) 2023; 13:2442. [PMID: 37510187 PMCID: PMC10377944 DOI: 10.3390/diagnostics13142442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 07/08/2023] [Accepted: 07/13/2023] [Indexed: 07/30/2023] Open
Abstract
Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model's capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.
Collapse
Affiliation(s)
- Bader Aldughayfiq
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| | - Farzeen Ashfaq
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - N Z Jhanjhi
- School of Computer Science, SCS, Taylor's University, Subang Jaya 47500, Malaysia
| | - Mamoona Humayun
- Department of Information Systems, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia
| |
Collapse
|
3
|
Method for Solving Difficulties in Rhythm Classification Caused by Few Samples and Similar Characteristics in Electrocardiograms. Bioengineering (Basel) 2023; 10:bioengineering10020196. [PMID: 36829690 PMCID: PMC9952353 DOI: 10.3390/bioengineering10020196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 01/31/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
A method for accurately analyzing electrocardiograms (ECGs), which are obtained from electrical signals generated by cardiac activity, is essential in heart disease diagnosis. However, rhythms are typically obtained with relatively few data samples and similar characteristics, making them difficult to classify. To solve these issues, we proposed a novel method that distinguishes a given ECG rhythm using a beat score map (BSM) image. Through the proposed method, the associations between beats and previously used features, such as the R-R interval, were considered. Rhythm classification was implemented by training a convolutional neural network model and using transfer learning with the created BSM image. As a result, the proposed method for ECG rhythms with small data samples showed significant results. It also showed good performance in differentiating atrial fibrillation (AFIB) and atrial flutter (AFL) rhythms, which are difficult to distinguish due to their similar characteristics. The performance for rhythms with a small number of samples of the proposed method is 20% better than an existing method. In addition, the performance based on the F-1 score for classifying AFIB and AFL of the proposed method is 30% better than the existing method. This study solved the previous limitations caused by small sample numbers and similar rhythms.
Collapse
|
4
|
Dimauro G, Griseta ME, Camporeale MG, Clemente F, Guarini A, Maglietta R. An intelligent non-invasive system for automated diagnosis of anemia exploiting a novel dataset. Artif Intell Med 2023; 136:102477. [PMID: 36710064 DOI: 10.1016/j.artmed.2022.102477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 12/19/2022] [Accepted: 12/19/2022] [Indexed: 12/27/2022]
Abstract
Anemia is a condition in which the oxygen-carrying capacity of red blood cells is insufficient to meet the body's physiological needs. It affects billions of people worldwide. An early diagnosis of this disease could prevent the advancement of other disorders. Traditional methods used to detect anemia consist of venipuncture, which requires a patient to frequently undergo laboratory tests. Therefore, anemia diagnosis using noninvasive and cost-effective methods is an open challenge. The pallor of the fingertips, palms, nail beds, and eye conjunctiva can be observed to establish whether a patient suffers from anemia. This article addresses the above challenges by presenting a novel intelligent system, based on machine learning, that supports the automated diagnosis of anemia. This system is innovative from different points of view. Specifically, it has been trained on a dataset that contains eye conjunctiva photos of Indian and Italian patients. This dataset, which was created using a very strict experimental set, is now made available to the Scientific Community. Moreover, compared to previous systems in the literature, the proposed system uses a low-cost device, which makes it suitable for widespread use. The performance of the learning algorithms utilizing two different areas of the mucous membrane of the eye is discussed. In particular, the RUSBoost algorithm, when appropriately trained on palpebral conjunctiva images, shows good performance in classifying anemic and nonanemic patients. The results are very robust, even when considering different ethnicities.
Collapse
Affiliation(s)
- Giovanni Dimauro
- Department of Computer Science, University of Bari 'Aldo Moro', Bari, Italy.
| | - Maria Elena Griseta
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy.
| | | | - Felice Clemente
- Haematology Dept. of National Cancer Institute 'Giovanni Paolo II', Bari, Italy.
| | - Attilio Guarini
- Haematology Dept. of National Cancer Institute 'Giovanni Paolo II', Bari, Italy.
| | - Rosalia Maglietta
- Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing, National Research Council of Italy, Bari, Italy.
| |
Collapse
|
5
|
Guessoum S, Belda S, Ferrandiz JM, Modiri S, Raut S, Dhar S, Heinkelmann R, Schuh H. The Short-Term Prediction of Length of Day Using 1D Convolutional Neural Networks (1D CNN). SENSORS (BASEL, SWITZERLAND) 2022; 22:9517. [PMID: 36502228 PMCID: PMC9740590 DOI: 10.3390/s22239517] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 11/04/2022] [Accepted: 11/29/2022] [Indexed: 06/17/2023]
Abstract
Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth's rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.
Collapse
Affiliation(s)
- Sonia Guessoum
- UAVAC, Department of Applied Mathematics, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain
| | - Santiago Belda
- UAVAC, Department of Applied Mathematics, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain
| | - Jose M. Ferrandiz
- UAVAC, Department of Applied Mathematics, Universidad de Alicante, Carretera San Vicente del Raspeig s/n, 03690 San Vicente del Raspeig, Alicante, Spain
| | - Sadegh Modiri
- Department Geodesy, Federal Agency for Cartography and Geodesy (BKG), 60322 Frankfurt am Main, Germany
| | - Shrishail Raut
- GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
- Institute for Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany
| | - Sujata Dhar
- GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
- Indian Institute of Technology Kanpur, Kanpur 208 016, Uttar Pradesh, India
| | | | - Harald Schuh
- GFZ German Research Centre for Geosciences, 14473 Potsdam, Germany
- Institute for Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany
| |
Collapse
|
6
|
Petmezas G, Stefanopoulos L, Kilintzis V, Tzavelis A, Rogers JA, Katsaggelos AK, Maglaveras N. State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint). JMIR Med Inform 2022; 10:e38454. [PMID: 35969441 PMCID: PMC9425174 DOI: 10.2196/38454] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/03/2022] [Accepted: 07/03/2022] [Indexed: 11/13/2022] Open
Abstract
Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic tools that can provide useful information regarding a patient’s health status. Deep learning (DL) is an area of intense exploration that leads the way in most attempts to create powerful diagnostic models based on physiological signals. Objective This study aimed to provide a systematic review of DL methods applied to ECG data for various clinical applications. Methods The PubMed search engine was systematically searched by combining “deep learning” and keywords such as “ecg,” “ekg,” “electrocardiogram,” “electrocardiography,” and “electrocardiology.” Irrelevant articles were excluded from the study after screening titles and abstracts, and the remaining articles were further reviewed. The reasons for article exclusion were manuscripts written in any language other than English, absence of ECG data or DL methods involved in the study, and absence of a quantitative evaluation of the proposed approaches. Results We identified 230 relevant articles published between January 2020 and December 2021 and grouped them into 6 distinct medical applications, namely, blood pressure estimation, cardiovascular disease diagnosis, ECG analysis, biometric recognition, sleep analysis, and other clinical analyses. We provide a complete account of the state-of-the-art DL strategies per the field of application, as well as major ECG data sources. We also present open research problems, such as the lack of attempts to address the issue of blood pressure variability in training data sets, and point out potential gaps in the design and implementation of DL models. Conclusions We expect that this review will provide insights into state-of-the-art DL methods applied to ECG data and point to future directions for research on DL to create robust models that can assist medical experts in clinical decision-making.
Collapse
Affiliation(s)
- Georgios Petmezas
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Leandros Stefanopoulos
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Vassilis Kilintzis
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Andreas Tzavelis
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States
| | - John A Rogers
- Department of Material Science, Northwestern University, Evanston, IL, United States
| | - Aggelos K Katsaggelos
- Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States
| | - Nicos Maglaveras
- Lab of Computing, Medical Informatics and Biomedical-Imaging Technologies, The Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| |
Collapse
|
7
|
Darmawahyuni A, Nurmaini S, Rachmatullah MN, Tutuko B, Sapitri AI, Firdaus F, Fansyuri A, Predyansyah A. Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification. PeerJ Comput Sci 2022; 8:e825. [PMID: 35174263 PMCID: PMC8802771 DOI: 10.7717/peerj-cs.825] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 12/01/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Electrocardiogram (ECG) signal classification plays a critical role in the automatic diagnosis of heart abnormalities. While most ECG signal patterns cannot be recognized by a human interpreter, they can be detected with precision using artificial intelligence approaches, making the ECG a powerful non-invasive biomarker. However, performing rapid and accurate ECG signal classification is difficult due to the low amplitude, complexity, and non-linearity. The widely-available deep learning (DL) method we propose has presented an opportunity to substantially improve the accuracy of automated ECG classification analysis using rhythm or beat features. Unfortunately, a comprehensive and general evaluation of the specific DL architecture for ECG analysis across a wide variety of rhythm and beat features has not been previously reported. Some previous studies have been concerned with detecting ECG class abnormalities only through rhythm or beat features separately. METHODS This study proposes a single architecture based on the DL method with one-dimensional convolutional neural network (1D-CNN) architecture, to automatically classify 24 patterns of ECG signals through both rhythm and beat. To validate the proposed model, five databases which consisted of nine-class of ECG-base rhythm and 15-class of ECG-based beat were used in this study. The proposed DL network was applied and studied with varying datasets with different frequency samplings in intra and inter-patient scheme. RESULTS Using a 10-fold cross-validation scheme, the performance results had an accuracy of 99.98%, a sensitivity of 99.90%, a specificity of 99.89%, a precision of 99.90%, and an F1-score of 99.99% for ECG rhythm classification. Additionally, for ECG beat classification, the model obtained an accuracy of 99.87%, a sensitivity of 96.97%, a specificity of 99.89%, a precision of 92.23%, and an F1-score of 94.39%. In conclusion, this study provides clinicians with an advanced methodology for detecting and discriminating heart abnormalities between different ECG rhythm and beat assessments by using one outstanding proposed DL architecture.
Collapse
Affiliation(s)
- Annisa Darmawahyuni
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Siti Nurmaini
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | | | - Bambang Tutuko
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Ade Iriani Sapitri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Firdaus Firdaus
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Ahmad Fansyuri
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| | - Aldi Predyansyah
- Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang, Indonesia
| |
Collapse
|
8
|
A novel approach for detection of consciousness level in comatose patients from EEG signals with 1-D convolutional neural network. Biocybern Biomed Eng 2022. [DOI: 10.1016/j.bbe.2021.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
9
|
Hudáky MG, Lehotay-Kéry P, Kiss A. A Novel Methodology for Measuring the Abstraction Capabilities of Image Recognition Algorithms. J Imaging 2021; 7:152. [PMID: 34460788 PMCID: PMC8404921 DOI: 10.3390/jimaging7080152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 08/03/2021] [Accepted: 08/17/2021] [Indexed: 12/04/2022] Open
Abstract
Creating a widely excepted model on the measure of intelligence became inevitable due to the existence of an abundance of different intelligent systems. Measuring intelligence would provide feedback for the developers and ultimately lead us to create better artificial systems. In the present paper, we show a solution where learning as a process is examined, aiming to detect pre-written solutions and separate them from the knowledge acquired by the system. In our approach, we examine image recognition software by executing different transformations on objects and detect if the software was resilient to it. A system with the required intelligence is supposed to become resilient to the transformation after experiencing it several times. The method is successfully tested on a simple neural network, which is not able to learn most of the transformations examined. The method can be applied to any image recognition software to test its abstraction capabilities.
Collapse
Affiliation(s)
- Márton Gyula Hudáky
- Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary; (M.G.H.); (P.L.-K.)
| | - Péter Lehotay-Kéry
- Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary; (M.G.H.); (P.L.-K.)
| | - Attila Kiss
- Department of Information Systems, ELTE Eötvös Loránd University, 1117 Budapest, Hungary; (M.G.H.); (P.L.-K.)
- Department of Informatics, J. Selye University, 94501 Komárno, Slovakia
| |
Collapse
|
10
|
N. Diniz D, T. Rezende M, G. C. Bianchi A, M. Carneiro C, J. S. Luz E, J. P. Moreira G, M. Ushizima D, N. S. de Medeiros F, J. F. Souza M. A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification. J Imaging 2021; 7:111. [PMID: 39080899 PMCID: PMC8321382 DOI: 10.3390/jimaging7070111] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 06/17/2021] [Accepted: 07/02/2021] [Indexed: 11/19/2022] Open
Abstract
In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals' workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.
Collapse
Affiliation(s)
- Débora N. Diniz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Mariana T. Rezende
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Andrea G. C. Bianchi
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Claudia M. Carneiro
- Departamento de Análises Clínicas, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (M.T.R.); (C.M.C.)
| | - Eduardo J. S. Luz
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Gladston J. P. Moreira
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| | - Daniela M. Ushizima
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA;
- Berkeley Institute for Data Science, University of California, Berkeley, CA 94720, USA
- Bakar Computational Health Sciences Institute, University of California, San Francisco, CA 94143, USA
| | - Fátima N. S. de Medeiros
- Departamento de Engenharia de Teleinformática, Universidade Federal do Ceará (UFC), Fortaleza 60455-970, Brazil;
| | - Marcone J. F. Souza
- Departamento de Computação, Universidade Federal de Ouro Preto (UFOP), Ouro Preto 35400-000, Brazil; (A.G.C.B.); (E.J.S.L.); (G.J.P.M.); (M.J.F.S.)
| |
Collapse
|
11
|
Zhang Y, Gorriz JM, Dong Z. Deep Learning in Medical Image Analysis. J Imaging 2021; 7:74. [PMID: 34460524 PMCID: PMC8321330 DOI: 10.3390/jimaging7040074] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 04/16/2021] [Indexed: 11/19/2022] Open
Abstract
Over recent years, deep learning (DL) has established itself as a powerful tool across a broad spectrum of domains in imaging-e [...].
Collapse
Affiliation(s)
- Yudong Zhang
- School of Informatics, University of Leicester, Leicester LE1 7RH, UK
| | - Juan Manuel Gorriz
- Department of Signal Theory, Telematics and Communications, University of Granada, 18071 Granada, Spain;
| | - Zhengchao Dong
- Molecular Imaging and Neuropathology Division, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA;
| |
Collapse
|