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Gaire S, Alsadoon A, Prasad PWC, Alsallami N, Bajaj SK, Dawoud A, VO TH. Enhanced cluster detection and noise reduction for geospatial time series data of COVID-19. Multimed Tools Appl 2023:1-32. [PMID: 37362721 PMCID: PMC10239308 DOI: 10.1007/s11042-023-15901-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/26/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023]
Abstract
Spatial-temporal analysis of the COVID-19 cases is critical to find its transmitting behaviour and to detect the possible emerging clusters. Poisson's prospective space-time analysis has been successfully implemented for cluster detection of geospatial time series data. However, its accuracy, number of clusters, and processing time are still a major problem for detecting small-sized clusters. The aim of this research is to improve the accuracy of cluster detection of COVID-19 at the county level in the U.S.A. by detecting small-sized clusters and reducing the noisy data. The proposed system consists of the Poisson prospective space-time analysis along with Enhanced cluster detection and noise reduction algorithm (ECDeNR) to improve the number of clusters and decrease the processing time. The results of accuracy, processing time, number of clusters, and relative risk are obtained by using different COVID-19 datasets in SaTScan. The proposed system increases the average number of clusters by 7 and the average relative risk by 9.19. Also, it provides a cluster detection accuracy of 91.35% against the current accuracy of 83.32%. It also gives a processing time of 5.69 minutes against the current processing time of 7.36 minutes on average. The proposed system focuses on improving the accuracy, number of clusters, and relative risk and reducing the processing time of the cluster detection by using ECDeNR algorithm. This study solves the issues of detecting the small-sized clusters at the early stage and enhances the overall cluster detection accuracy while decreasing the processing time.
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Affiliation(s)
- Sabitri Gaire
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Wagga Wagga, Australia
| | - Abeer Alsadoon
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Wagga Wagga, Australia
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
- Asia Pacific International College (APIC), Sydney, Australia
| | - P. W. C. Prasad
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Wagga Wagga, Australia
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
| | - Nada Alsallami
- Computer Science Department, Worcester State University, Worcester, MA USA
| | - Simi Kamini Bajaj
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
| | - Ahmed Dawoud
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
| | - Trung Hung VO
- University of Technology and Education - The University of Danang (UTE-UDN), Danang, Viet Nam
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2
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Maharjan P, Alsadoon A, Prasad PWC, Al-Khalil AB, Jerew OD, Alsadoon G, Chapagain B. An enhanced algorithm for improving real-time video transmission for tele-training education. Multimed Tools Appl 2022; 81:8409-8428. [PMID: 35125927 PMCID: PMC8809212 DOI: 10.1007/s11042-022-12045-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 06/25/2021] [Accepted: 01/03/2022] [Indexed: 06/14/2023]
Abstract
Tele-training in surgical education has not been effectively implemented. There is a stringent need for a high transmission rate, reliability, throughput, and reduced distortion for high-quality video transmission in the real-time network. This work aims to propose a system that improves video quality during real-time surgical tele-training. The proposed approach aims to minimise the video frame's total distortion, ensuring better flow rate allocation and enhancing the video frames' reliability. The proposed system consists of a proposed algorithm for Enhancing Video Quality, Distorting Minimization, Bandwidth efficiency, and Reliability Maximization called (EVQDMBRM) algorithm. The proposed algorithm reduces the video frame's total distortion. In addition, it enhances the video quality in a real-time network by dynamically allocating the flow rate at the video source and maximizing the transmission reliability of the video frames. The result shows that the proposed EVQDMBRM algorithm improves the video quality with the minimized total distortion. Therefore, it improves the Peak Signal to Noise Ratio (PSNR) average by 51.13 dB against 47.28 dB in the existing systems. Furthermore, it reduces the video frames processing time average by 58.2 milliseconds (ms) against 76.1, and the end-to-end delay average by 114.57 ms against 133.58 ms comparing to the traditional methods. The proposed system concentrates on minimizing video distortion and improving the surgical video transmission quality by using an EVQDMBRM algorithm. It provides the mechanism to allocate the video rate at the source dynamically. Besides that, it minimizes the packet loss ratio and probing status, which estimates the available bandwidth.
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Affiliation(s)
- Pooja Maharjan
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Sydney, Australia
| | - Abeer Alsadoon
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Sydney, Australia
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
- Kent Institute Australia, Sydney, Australia
- Asia Pacific International College (APIC), Sydney, Australia
| | - P. W. C. Prasad
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Sydney, Australia
- School of Computer Data and Mathematical Sciences, Western Sydney University (WSU), Sydney, Australia
- Kent Institute Australia, Sydney, Australia
- Australian Institute of Higher Education, Sydney, Australia
| | - Ahmad B. Al-Khalil
- College of Science, Department of Computer Science, The University of Duhok, Duhok, KRG Iraq
| | - Oday D. Jerew
- Asia Pacific International College (APIC), Sydney, Australia
| | - Ghossoon Alsadoon
- Business Informatics Department, AMA International University Bahrain (AMAIUB), Salmabad, Bahrain
| | - Binod Chapagain
- School of Computing Mathematics and Engineering, Charles Sturt University (CSU), Sydney, Australia
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3
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Thapa A, Alsadoon A, Prasad PWC, Bajaj S, Alsadoon OH, Rashid TA, Ali RS, Jerew OD. Deep learning for breast cancer classification: Enhanced tangent function. Comput Intell 2021. [DOI: 10.1111/coin.12476] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Affiliation(s)
- Ashu Thapa
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
- Kent Institute Australia Sydney Australia
- Asia Pacific International College (APIC) Sydney Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics Charles Sturt University (CSU) Wagga Wagga Australia
| | - Simi Bajaj
- School of Computer Data and Mathematical Sciences University of Western Sydney (UWS) Sydney Australia
| | | | - Tarik A. Rashid
- Computer Science and Engineering University of Kurdistan Hewler Erbil KRG IRAQ
| | - Rasha S. Ali
- Department of Computer Techniques Engineering AL Nisour University College Baghdad Iraq
| | - Oday D. Jerew
- Asia Pacific International College (APIC) Sydney Australia
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4
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Maharjan N, Alsadoon A, Prasad PWC, Abdullah S, Rashid TA. A novel visualization system of using augmented reality in knee replacement surgery: Enhanced bidirectional maximum correntropy algorithm. Int J Med Robot 2021; 17:e2223. [PMID: 33421286 DOI: 10.1002/rcs.2223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND AIM Image registration and alignment are the main limitations of augmented reality (AR)-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY Markerless image registration method was used for AR-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right-to-left direction and non-border occlusion from left-to-right direction. RESULTS This study has improved video precision to 0.57-0.61 mm alignment error. Furthermore, with the use of bidirectional points, that is, forward and backward directional cloud point, the iteration on image registration was decreased. This has led to improve the processing time as well. The processing time of video frames was improved to 7.4-11.74 frames per second. CONCLUSIONS It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by the movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed AR system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels and so forth.
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Affiliation(s)
- Nitish Maharjan
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia.,School of Computer Data and Mathematical Sciences, University of Western Sydney (UWS), Sydney, Australia.,School of Information Technology, Southern Cross University (SCU), Sydney, Australia.,Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia.,Kent Institute Australia, Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney Campus, Wagga Wagga, Australia
| | - Salma Abdullah
- Department of Computer Engineering, University of Technology, Baghdad, Iraq
| | - Tarik A Rashid
- Asia Pacific International College (APIC), Information Technology Department, Sydney, Australia
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5
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Maharjan A, Alsadoon A, Prasad PWC, AlSallami N, Rashid TA, Alrubaie A, Haddad S. A novel solution of using mixed reality in bowel and oral and maxillofacial surgical telepresence: 3D mean value cloning algorithm. Int J Med Robot 2021; 17:e2224. [PMID: 33426753 DOI: 10.1002/rcs.2224] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND AIM Most of the mixed reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY The proposed system enhanced mean-value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the three-dimensional mean-value coordinates and improvised mean-value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discolouration artefacts around the blending region. RESULTS The accuracy in terms of overlay error of the proposed solution is improved from 1.01 to 0.80 mm, whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 s from 0.211 s. The processing time and the accuracy of the proposed solution are enhanced as compared to the state-of-art solution. CONCLUSION Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video.
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Affiliation(s)
- Arjina Maharjan
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia.,School of Computer Data and Mathematical Sciences, University of Western Sydney (UWS), Sydney, Australia.,School of Information Technology, Southern Cross University (SCU), Sydney, Australia.,Information Technology Department, Asia Pacific International College (APIC), Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University (CSU), Sydney, Australia
| | - Nada AlSallami
- Computer Science Department, Worcester State University, Massachusetts, USA
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, Iraq
| | - Ahmad Alrubaie
- Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Australia.,Department of Oral and Maxillofacial Services, Central Coast Area Health, Australia
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6
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Chhetri S, Alsadoon A, Al‐Dala'in T, Prasad PWC, Rashid TA, Maag A. Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow. Comput Intell 2020. [DOI: 10.1111/coin.12428] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Sagar Chhetri
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Thair Al‐Dala'in
- School of Computing and Mathematics Charles Sturt University Sydney Australia
- School of Computing Engineering and Mathematics Western Sydney University Sydney Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics Charles Sturt University Sydney Australia
| | - Tarik A. Rashid
- Computer Science and Engineering University of Kurdistan Hewler Erbil, KRG IRAQ
| | - Angelika Maag
- School of Computing and Mathematics Charles Sturt University Sydney Australia
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7
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Sharma K, Alsadoon A, Prasad PWC, Al-Dala'in T, Nguyen TQV, Pham DTH. A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction. Comput Methods Programs Biomed 2020; 197:105751. [PMID: 32957061 DOI: 10.1016/j.cmpb.2020.105751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 09/05/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND AND AIM deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem. METHODOLOGY the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection. RESULTS four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average. CONCLUSION the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.
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Affiliation(s)
- Kiran Sharma
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia.
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Thair Al-Dala'in
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Tran Quoc Vinh Nguyen
- The University of Da Nang - University of Science and Education, Faculty of Information Technology, Vietnam
| | - Duong Thu Hang Pham
- The University of Da Nang - University of Science and Education, Faculty of Information Technology, Vietnam
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8
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Maharjan A, Alsadoon A, Prasad PWC, AlSallami N, Rashid TA, Alrubaie A, Haddad S. A Novel Solution of Using Mixed Reality in Bowel and Oral and Maxillofacial Surgical Telepresence: 3D Mean Value Cloning algorithm. Int J Med Robot 2020:e2161. [PMID: 32886412 DOI: 10.1002/rcs.2161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 09/01/2020] [Accepted: 09/01/2020] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND AIM Most of the Mixed Reality models used in the surgical telepresence are suffering from the discrepancies in the boundary area and spatial-temporal inconsistency due to the illumination variation in the video frames. The aim behind this work is to propose a new solution that helps produce the composite video by merging the augmented video of the surgery site and virtual hand of the remote expertise surgeon. The purpose of the proposed solution is to decrease the processing time and enhance the accuracy of merged video by decreasing the overlay and visualization error and removing occlusion and artefacts. METHODOLOGY The proposed system enhanced the mean value cloning algorithm that helps to maintain the spatial-temporal consistency of the final composite video. The enhanced algorithm includes the 3D mean value coordinates and improvised mean value interpolant in the image cloning process, which helps to reduce the sawtooth, smudging and discoloration artefacts around the blending region RESULTS: As compared to the state of art solution, the accuracy in terms of overlay error of the proposed solution is improved from 1.01mm to 0.80mm whereas the accuracy in terms of visualization error is improved from 98.8% to 99.4%. The processing time is reduced to 0.173 seconds from 0.211 seconds CONCLUSION: Our solution helps make the object of interest consistent with the light intensity of the target image by adding the space distance that helps maintain the spatial consistency in the final merged video. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Arjina Maharjan
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
| | - Nada AlSallami
- Computer Science Department, Worcester State University, MA, USA
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, IRAQ
| | - Ahmad Alrubaie
- Faculty of Medicine, University of New South Wales, Sydney, Australia
| | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Australia
- Department of Oral and Maxillofacial Services, Central Coast Area Health, Australia
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Maharjan N, Alsadoon A, Prasad PWC, Abdullah S, Rashid TA. A Novel Visualization System of Using Augmented Reality in Knee Replacement Surgery: Enhanced Bidirectional Maximum CorrentropyAlgorithm. Int J Med Robot 2020:e2154. [PMID: 32875672 DOI: 10.1002/rcs.2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Revised: 08/18/2020] [Accepted: 08/19/2020] [Indexed: 11/11/2022]
Abstract
BACKGROUND AND AIM Image registration and alignment are the main limitations of augmented reality-based knee replacement surgery. This research aims to decrease the registration error, eliminate outcomes that are trapped in local minima to improve the alignment problems, handle the occlusion and maximize the overlapping parts. METHODOLOGY markerless image registration method was used for Augmented reality-based knee replacement surgery to guide and visualize the surgical operation. While weight least square algorithm was used to enhance stereo camera-based tracking by filling border occlusion in right to left direction and non-border occlusion from left to right direction. RESULTS This study has improved video precision to 0.57 mm ∼ 0.61 mm alignment error. Furthermore, with the use of bidirectional points, i.e. Forwards and backwards directional cloud point, the iteration on image registration was decreased. This has led to improved the processing time as well. The processing time of video frames was improved to 7.4 ∼11.74 fps. CONCLUSIONS It seems clear that this proposed system has focused on overcoming the misalignment difficulty caused by movement of patient and enhancing the AR visualization during knee replacement surgery. The proposed system was reliable and favourable which helps in eliminating alignment error by ascertaining the optimal rigid transformation between two cloud points and removing the outliers and non-Gaussian noise. The proposed augmented reality system helps in accurate visualization and navigation of anatomy of knee such as femur, tibia, cartilage, blood vessels, etc. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Nitish Maharjan
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Australia
- Department of Information Technology, Study Group Australia, Sydney Campus, Australia
| | - Salma Abdullah
- Department of Computer Engineering, University of Technology, Baghdad, Iraq
| | - Tarik A Rashid
- Computer Science and Engineering, University of Kurdistan Hewler, Erbil, KRG, IRAQ
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Basnet J, Alsadoon A, Prasad PWC, Aloussi SA, Alsadoon OH. A Novel Solution of Using Deep Learning for White Blood Cells Classification: Enhanced Loss Function with Regularization and Weighted Loss (ELFRWL). Neural Process Lett 2020. [DOI: 10.1007/s11063-020-10321-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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Tuladhar S, AlSallami N, Alsadoon A, Prasad PWC, Alsadoon OH, Haddad S, Alrubaie A. A recent review and a taxonomy for hard and soft tissue visualization-based mixed reality. Int J Med Robot 2020; 16:1-22. [PMID: 32388923 DOI: 10.1002/rcs.2120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 04/28/2020] [Accepted: 04/30/2020] [Indexed: 11/10/2022]
Abstract
BACKGROUND Mixed reality (MR) visualization is gaining popularity in image-guided surgery (IGS) systems, especially for hard and soft tissue surgeries. However, a few MR systems are implemented in real time. Some factors are limiting MR technology and creating a difficulty in setting up and evaluating the MR system in real environments. Some of these factors include: the end users are not considered, the limitations in the operating room, and the medical images are not fully unified into the operating interventions. METHODOLOGY The purpose of this article is to use Data, Visualization processing, and View (DVV) taxonomy to evaluate the current MR systems. DVV includes all the components required to be considered and validated for the MR used in hard and soft tissue surgeries. This taxonomy helps the developers and end users like researchers and surgeons to enhance MR system for the surgical field. RESULTS We evaluated, validated, and verified the taxonomy based on system comparison, completeness, and acceptance criteria. Around 24 state-of-the-art solutions that are picked relate to MR visualization, which is then used to demonstrate and validate this taxonomy. The results showed that most of the findings are evaluated and others are validated. CONCLUSION The DVV taxonomy acts as a great resource for MR visualization in IGS. State-of-the-art solutions are classified, evaluated, validated, and verified to elaborate the process of MR visualization during surgery. The DVV taxonomy provides the benefits to the end users and future improvements in MR.
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Affiliation(s)
- Selina Tuladhar
- School of Computing and Mathematics, Charles Sturt University, Sydney, New South Wales, Australia
| | - Nada AlSallami
- Computer Science Department, Worcester State University, Worcester, Massachusetts, USA
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, New South Wales, Australia.,Department of Information Technology, Study Group Australia, Sydney, New South Wales, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney, New South Wales, Australia
| | - Omar H Alsadoon
- Department of Islamic Sciences, Al Iraqia University, Baghdad, Iraq
| | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Sydney, New South Wales, Australia.,Department of Oral and Maxillofacial Services, Central Coast Area Health, Gosford, New South Wales, Australia
| | - Ahmad Alrubaie
- Faculty of Medicine, University of New South Wales, Sydney, New South Wales, Australia
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12
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Maharjan S, Alsadoon A, Prasad PWC, Al-Dalain T, Alsadoon OH. A novel enhanced softmax loss function for brain tumour detection using deep learning. J Neurosci Methods 2019; 330:108520. [PMID: 31734325 DOI: 10.1016/j.jneumeth.2019.108520] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 10/22/2019] [Accepted: 11/11/2019] [Indexed: 10/25/2022]
Abstract
BACKGROUND AND AIM In deep learning, the sigmoid function is unsuccessfully used for the multiclass classification of the brain tumour due to its limit of binary classification. This study aims to increase the classification accuracy by reducing the risk of overfitting problem and supports multi-class classification. The proposed system consists of a convolutional neural network with modified softmax loss function and regularization. RESULTS Classification accuracy for the different types of tumours and the processing time were calculated based on the probability score of the labeled data and their execution time. Different accuracy values and processing time were obtained when testing the proposed system using different samples of MRI images. The result shows that the proposed solution is better compared to the other systems. Besides, the proposed solution has higher accuracy by almost 2 % and less processing time of 40∼50 ms compared to other current solutions. CONCLUSION The proposed system focused on classification accuracy of the different types of tumours from the 3D MRI images. This paper solves the issues of binary classification, the processing time, and the issues of overfitting of the data.
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13
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Bhandari A, Prasad PWC, Alsadoon A, Maag A. Object detection and recognition: using deep learning to assist the visually impaired. Disabil Rehabil Assist Technol 2019; 16:280-288. [PMID: 31694420 DOI: 10.1080/17483107.2019.1673834] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Abstract
BACKGROUND Deep learning systems have improved performance of devices through more accurate object detection in a significant number of areas, for medical aid in general, and also for navigational aids for the visually impaired. Systems addressing different needs are available, and many manage effectively the detection of static obstacles. PURPOSE This research provides a review of deep learning systems used with navigational tools for the visually Impaired and a framework for guidance for future research. METHODS We compare current deep learning systems used with navigational tools for the visually impaired and compile a taxonomy of indispensable features for systems. RESULTS Challenges to detection. Our taxonomy of improved navigational systems shows that it is sufficiently robust to be generally applied. CONCLUSION This critical analysis is, to the best of our knowledge, the first of its kind and will provide a much-needed overview of the field.Implication for RehabilitationDeep learning systems can provide lost cost solutions for the visually impaired.Of these, convolutional neural networks (CNN) and fully convolutional neural networks (FCN) show great promise in terms of the development of multifunctional technology for the visually impaired (i.e., being less specific task oriented).CNN have also potential for overcoming challenges caused by moving and occluded objects.This work has also highlighted a need for greater emphasis on feedback to the visually impaired which for many technologies is limited.
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Affiliation(s)
- Abinash Bhandari
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - Angelika Maag
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
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Venkata HS, Alsadoon A, Prasad PWC, Alsadoon OH, Haddad S, Deva A, Hsu J. A novel mixed reality in breast and constructive jaw surgical tele-presence. Comput Methods Programs Biomed 2019; 177:253-268. [PMID: 31319954 DOI: 10.1016/j.cmpb.2019.05.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/25/2019] [Accepted: 05/28/2019] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND AIM Surgical telepresence has been implemented using Mixed reality (MR) but, MR is theory based and only used for investigating research. The Aim of this paper is to propose and implement a new solution by merging augmented video (generating in local site) and virtual expertise surgeon hand (remote site). This system is to improve the visualization of surgical area, overlay accuracy in the merged video without having any discoloured patterns on hand, smudging artefacts on surgeon hand boundary and occluded areas of surgical area. METHODOLOGY The Proposed system consists of an Enhanced Multi-Layer Mean Value Cloning (EMLMV) algorithm that improves the overlay accuracy, visualization accuracy and the processing time. This proposed algorithm includes trimap and alpha matting as a pre-processing stage of merging process, which helps to remove the smudging and discoloured artefacts surrounded by remote surgeon hand. RESULTS Results showing that the proposed system improved the accuracy by reducing the overlay error of merging image from 1.3 mm (Millimeter) to 0.9 mm. Furthermore, it improves the visibility of surgeon hand in the final merged image from 98.4% (visibility of pixels) to 99.1% (visibility of pixels). Similarly, the processing time in our proposed solution is reduced, which is computed as 10 s to produce 50 frames, whilst, the state of art solution computes 11 s for the same number of frames. CONCLUSION The proposed system focuses on the merging of augmented reality video (local site), and the virtual reality video (remote site) with the accurate visualization. we consider discoloured areas, smudging artefacts and occlusion as the main aspects to improve the accuracy of merged video in terms of overlay error and visualization error. So, the proposed system would produce the merged video with the removal of artefacts around the expert surgeon hand.
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Affiliation(s)
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia.
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | | | - Sami Haddad
- Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Sydney, Australia; Department of Oral and Maxillofacial Services, Central Coast Area Health, Australia
| | - Anand Deva
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
| | - Jeremy Hsu
- Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia
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15
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Pokhrel S, Alsadoon A, Prasad PWC, Paul M. A novel augmented reality (AR) scheme for knee replacement surgery by considering cutting error accuracy. Int J Med Robot 2018; 15:e1958. [DOI: 10.1002/rcs.1958] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Revised: 07/25/2018] [Accepted: 08/17/2018] [Indexed: 12/25/2022]
Affiliation(s)
- Suraj Pokhrel
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - P W C Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - Manoranjan Paul
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
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16
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Segura Anaya LH, Alsadoon A, Costadopoulos N, Prasad PWC. Ethical Implications of User Perceptions of Wearable Devices. Sci Eng Ethics 2018; 24:1-28. [PMID: 28155094 DOI: 10.1007/s11948-017-9872-8] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2016] [Accepted: 01/08/2017] [Indexed: 06/06/2023]
Abstract
Health Wearable Devices enhance the quality of life, promote positive lifestyle changes and save time and money in medical appointments. However, Wearable Devices store large amounts of personal information that is accessed by third parties without user consent. This creates ethical issues regarding privacy, security and informed consent. This paper aims to demonstrate users' ethical perceptions of the use of Wearable Devices in the health sector. The impact of ethics is determined by an online survey which was conducted from patients and users with random female and male division. Results from this survey demonstrate that Wearable Device users are highly concerned regarding privacy issues and consider informed consent as "very important" when sharing information with third parties. However, users do not appear to relate privacy issues with informed consent. Additionally, users expressed the need for having shorter privacy policies that are easier to read, a more understandable informed consent form that involves regulatory authorities and there should be legal consequences the violation or misuse of health information provided to Wearable Devices. The survey results present an ethical framework that will enhance the ethical development of Wearable Technology.
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Affiliation(s)
| | - Abeer Alsadoon
- Studygroup Australia, Level 1, 64, Oxford Street, Darlinghurst, Sydney, 2010, Australia
| | - N Costadopoulos
- Studygroup Australia, Level 1, 64, Oxford Street, Darlinghurst, Sydney, 2010, Australia
| | - P W C Prasad
- Studygroup Australia, Level 1, 64, Oxford Street, Darlinghurst, Sydney, 2010, Australia.
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Vu LG, Alsadoon A, Prasad PWC, Rahma AMS. Improving Accuracy in Face Recognition Proposal to Create a Hybrid Photo Indexing Algorithm, Consisting of Principal Component Analysis and a Triangular Algorithm (PCAaTA). INT J PATTERN RECOGN 2017. [DOI: 10.1142/s0218001417560018] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Accurate face recognition is today vital, principally for reasons of security. Current methods employ algorithms that index (classify) important features of human faces. There are many current studies in this field but most current solutions have significant limitations. Principal Component Analysis (PCA) is one of the best facial recognition algorithms. However, there are some noises that could affect the accuracy of this algorithm. The PCA works well with the support of preprocessing steps such as illumination reduction, background removal and color conversion. Some current solutions have shown results when using a combination of PCA and preprocessing steps. This paper proposes a hybrid solution in face recognition using PCA as the main algorithm with the support of a triangular algorithm in face normalization in order to enhance indexing accuracy. To evaluate the accuracy of the proposed hybrid indexing algorithm, the PCAaTA is tested and the results are compared with current solutions.
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Affiliation(s)
- L. G. Vu
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - Abeer Alsadoon
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - P. W. C. Prasad
- School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
| | - A. M. S. Rahma
- Computer Science Department, University of Technology, Baghdad, Iraq
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