The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, which was tested on CT images for COVID-19 identification. For our lightweight model, we used a stack of two 3 x 3 convolution layers (without spatial pooling in between) instead of a single 3 x 3 convolution layer as proposed in Anam-Net to increase the receptive field and to reduce the trainable parameters. The proposed method includes fewer filters in all convolutional layers than the original Anam-Net and does not have an increasing number of filters for decreasing resolution. These modifications do not compromise on the segmentation accuracy, but they do make the architecture significantly lighter in terms of the number of trainable parameters and computation time. The proposed architecture has comparatively fewer parameters (1.01M) than Anam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the other recent works. The proposed model does not require any problem-specific pre- or post-processing, nor does it rely on handcrafted features. In addition, the attribute of being efficient in terms of segmentation accuracy as well as lightweight makes the proposed method a suitable candidate to be used in the screening platforms at the point of care. We evaluated our proposed model on open-access datasets namely, DRIVE, STARE, and CHASE_DB. The experimental results show that the proposed model outperforms several state-of-the-art methods, such as U-Net and its variants, fully convolutional network (FCN), SegNet, CCNet, ResWNet, residual connection-based encoder-decoder network (RCED-Net), and scale-space approx. network (SSANet) in terms of {dice coefficient, sensitivity (SN), accuracy (ACC), and the area under the ROC curve (AUC)} with the scores of {0.8184, 0.8561, 0.9669, and 0.9868} on the DRIVE dataset, the scores of {0.8233, 0.8581, 0.9726, and 0.9901} on the STARE dataset, and the scores of {0.8138, 0.8604, 0.9752, and 0.9906} on the CHASE_DB dataset. Additionally, we perform cross-training experiments on the DRIVE and STARE datasets. The result of this experiment indicates the generalization ability and robustness of the proposed model.
{"title":"Modified Anam-Net Based Lightweight Deep Learning Model for Retinal Vessel Segmentation","authors":"Syed Irtaza Haider, Khursheed Aurangzeb, Musaed A. Alhussein","doi":"10.32604/cmc.2022.025479","DOIUrl":"https://doi.org/10.32604/cmc.2022.025479","url":null,"abstract":"The accurate segmentation of retinal vessels is a challenging task due to the presence of various pathologies as well as the low-contrast of thin vessels and non-uniform illumination. In recent years, encoder-decoder networks have achieved outstanding performance in retinal vessel segmentation at the cost of high computational complexity. To address the aforementioned challenges and to reduce the computational complexity, we propose a lightweight convolutional neural network (CNN)-based encoder-decoder deep learning model for accurate retinal vessels segmentation. The proposed deep learning model consists of encoder-decoder architecture along with bottleneck layers that consist of depth-wise squeezing, followed by full-convolution, and finally depth-wise stretching. The inspiration for the proposed model is taken from the recently developed Anam-Net model, which was tested on CT images for COVID-19 identification. For our lightweight model, we used a stack of two 3 x 3 convolution layers (without spatial pooling in between) instead of a single 3 x 3 convolution layer as proposed in Anam-Net to increase the receptive field and to reduce the trainable parameters. The proposed method includes fewer filters in all convolutional layers than the original Anam-Net and does not have an increasing number of filters for decreasing resolution. These modifications do not compromise on the segmentation accuracy, but they do make the architecture significantly lighter in terms of the number of trainable parameters and computation time. The proposed architecture has comparatively fewer parameters (1.01M) than Anam-Net (4.47M), U-Net (31.05M), SegNet (29.50M), and most of the other recent works. The proposed model does not require any problem-specific pre- or post-processing, nor does it rely on handcrafted features. In addition, the attribute of being efficient in terms of segmentation accuracy as well as lightweight makes the proposed method a suitable candidate to be used in the screening platforms at the point of care. We evaluated our proposed model on open-access datasets namely, DRIVE, STARE, and CHASE_DB. The experimental results show that the proposed model outperforms several state-of-the-art methods, such as U-Net and its variants, fully convolutional network (FCN), SegNet, CCNet, ResWNet, residual connection-based encoder-decoder network (RCED-Net), and scale-space approx. network (SSANet) in terms of {dice coefficient, sensitivity (SN), accuracy (ACC), and the area under the ROC curve (AUC)} with the scores of {0.8184, 0.8561, 0.9669, and 0.9868} on the DRIVE dataset, the scores of {0.8233, 0.8581, 0.9726, and 0.9901} on the STARE dataset, and the scores of {0.8138, 0.8604, 0.9752, and 0.9906} on the CHASE_DB dataset. Additionally, we perform cross-training experiments on the DRIVE and STARE datasets. The result of this experiment indicates the generalization ability and robustness of the proposed model.","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126567256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.
{"title":"Data Optimization in IoT-Assisted Sensor Networks on Cloud Platform","authors":"G. Suseendran, D. Akila, Souvik Pal, Bikramjit Sarkar, A. Aly, Dac-Nhuong Le","doi":"10.21203/RS.3.RS-269814/V1","DOIUrl":"https://doi.org/10.21203/RS.3.RS-269814/V1","url":null,"abstract":"\u0000 This article presents a new scheme for data optimization in IoT assister sensor networks. The various components of IoT assisted cloud platform are discussed. In addition, a new architecture for IoT assisted sensor networks is presented. Further, a model for data optimization in IoT assisted sensor networks is proposed. A novel Membership inducing Dynamic Data Optimization (MIDDO) algorithm for IoT assisted sensor network is proposed in this research. The proposed algorithm considers every node data and utilized membership function for the optimized data allocation. The proposed framework is compared with two stage optimization, dynamic stochastic optimization and sparsity inducing optimization and evaluated in terms of performance ratio, reliability ratio, coverage ratio and sensing error. It was inferred that the proposed MIDDO algorithm achieves an average performance ratio of 76.55%, reliability ratio of 94.74%, coverage ratio of 85.75% and sensing error of 0.154.","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116786303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Approximation for the Entropy Measuring in the General Structure of燬GSP3","authors":"Kamel Jebreen, Muhammad Haroon Aftab, Mohammad Issa Sowaity, Zeeshan Saleem Mufti, Muhammad Hussain","doi":"10.32604/cmc.2022.030246","DOIUrl":"https://doi.org/10.32604/cmc.2022.030246","url":null,"abstract":"","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115001771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Design of Machine Learning Based Smart Irrigation System for Precision Agriculture","authors":"Khalil Ibrahim Mohammad Abuzanouneh, Fahd N Al-Wesabi, Amani Abdulrahman Albraikan, Mesfer Al Duhayyim, M. Al-Shabi, Anwer Mustafa Hilal, Manar Ahmed Hamza, Abu Sarwar Zamani, K. Muthulakshmi","doi":"10.32604/cmc.2022.022648","DOIUrl":"https://doi.org/10.32604/cmc.2022.022648","url":null,"abstract":"","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115168242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Transfer Learning for Disease Diagnosis from Myocardial Perfusion SPECT營maging","authors":"Phung Nhu Hai, Nguyen Chi Thanh, Nguyen Thanh Trung, Tran Trung Kien","doi":"10.32604/cmc.2022.031027","DOIUrl":"https://doi.org/10.32604/cmc.2022.031027","url":null,"abstract":"","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115301321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO2 gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.
由于私家车数量的增加，Suranaree理工大学(SUT)校园内的交通事故数量有所增加。本文针对共享经济模式下私家车的局限性，重点研究了基于智能交通系统(ITS)的公共交通系统的发展。SUT Smart Transit是主要的公共交通系统，而MoreSai@SUT(电动摩托车服务)是次要的公共交通系统。作为一个组合，它们被称为多模式运输系统。此外，采用车辆到网络(V2N)技术开发校园多模式交通系统。由于车辆配备了车载单元(OBU)和4G LTE模块，因此可以将实时速度和位置传输到云端。然后将数据应用于所提出的估计到达时间(ETA)的数学模型。在车辆分类和计数方面，我们部署了闭路电视摄像机，并使用You Only Look Once (YOLO)算法对录制的视频进行分析。讨论了2019冠状病毒病大流行前SUT智能交通和MoreSai@SUT的仿真和测量结果。与已有的研究相反，本文提出的系统是在真实环境中实现的。最终结果揭示了用户的吸引力和满意度。同时，该系统在校园实际实施多模式交通时减少了二氧化碳排放。
{"title":"A Multi-Mode Public Transportation System Using Vehicular to Network Architecture","authors":"S. Poochaya, P. Uthansakul, M. Uthansakul, Patikorn Anchuen, K. Thammakul, Arfat Ahmad Khan, Niwat Punanwarakorn, Pech Sirivoratum, A. Kaewkrad, Panrawee Kanpan, Apichart Wantamee","doi":"10.32604/cmc.2022.031162","DOIUrl":"https://doi.org/10.32604/cmc.2022.031162","url":null,"abstract":"The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO2 gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115382237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve","authors":"Mudita Uppal, Deepali Gupta, Divya Anand, Fahd S. Alharithi, Jasem Almotiri, Arturo Mansilla, Dinesh Singh, N. Goyal","doi":"10.32604/cmc.2022.025330","DOIUrl":"https://doi.org/10.32604/cmc.2022.025330","url":null,"abstract":"","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123097552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"An Innovative Bispectral Deep Learning Method for Protein Family Classification","authors":"Isam Abu-Qasmieh, Amjed Al Fahoum, Hiam Alquran, Ala’a Zyout","doi":"10.32604/cmc.2023.037431","DOIUrl":"https://doi.org/10.32604/cmc.2023.037431","url":null,"abstract":"","PeriodicalId":329824,"journal":{"name":"Computers, Materials & Continua","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116672087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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