Wenbin Wu¹, Guanjun Liu^1,*, Kaiyi Liang², Hui Zhou²

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 1323-1335, 2023, DOI:10.32604/cmes.2022.020428 - 31 August 2022

Abstract Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction. U-Net has been the baseline model since the very beginning due to a symmetrical U-structure for better feature extraction and fusing and suitable for small datasets. To enhance the segmentation performance of U-Net, cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine. However, the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot… More >

Yingpeng Dai¹, Junzheng Wang¹, Jing Li^1,*, Lingfeng Meng², Songfeng Wang^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 135-156, 2023, DOI:10.32604/cmes.2022.021525 - 24 August 2022

Abstract It is important for the autonomous system to understand environmental information. For the autonomous system, it is desirable to have a strong generalization ability to deal with different complex environmental information, as well as have high accuracy and quick inference speed. Network ensemble architecture is a good choice to improve network performance. However, it is unsuitable for real-time applications on the autonomous system. To tackle this problem, a new neural network ensemble named partial-shared ensemble network (PSENet) is presented. PSENet changes network ensemble architecture from parallel architecture to scatter architecture and merges multiple component networks More >

Baolin Sun^1,#, Xuesong Gao^2,#, Weiqiang Chen², Qihao Sun², Xiaoxiao Cui¹, Hao Guo¹, Cishahayo Remesha Kevin¹, Shuaishuai Liu², Zhi Liu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 383-403, 2023, DOI:10.32604/cmes.2022.020491 - 24 August 2022

Abstract The mixed reality conference system proposed in this paper is a robust, real-time video conference application software that makes up for the simple interaction and lack of immersion and realism of traditional video conference, which realizes the entire process of holographic video conference from client to cloud to the client. This paper mainly focuses on designing and implementing a video conference system based on AI segmentation technology and mixed reality. Several mixed reality conference system components are discussed, including data collection, data transmission, processing, and mixed reality presentation. The data layer is mainly used for… More >

R. Radha^1,*, R. Gopalakrishnan²

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3113-3127, 2023, DOI:10.32604/iasc.2023.030667 - 17 August 2022

Abstract In the field of diagnosis of medical images the challenge lies in tracking and identifying the defective cells and the extent of the defective region within the complex structure of a brain cavity. Locating the defective cells precisely during the diagnosis phase helps to fight the greatest exterminator of mankind. Early detection of these defective cells requires an accurate computer-aided diagnostic system (CAD) that supports early treatment and promotes survival rates of patients. An earlier version of CAD systems relies greatly on the expertise of radiologist and it consumed more time to identify the defective… More >

Abu Saleh Musa Miah¹, Jungpil Shin^1,*, Md. Al Mehedi Hasan¹, Md Abdur Rahim², Yuichi Okuyama¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2521-2536, 2023, DOI:10.32604/csse.2023.029336 - 01 August 2022

Abstract Communication between people with disabilities and people who do not understand sign language is a growing social need and can be a tedious task. One of the main functions of sign language is to communicate with each other through hand gestures. Recognition of hand gestures has become an important challenge for the recognition of sign language. There are many existing models that can produce a good accuracy, but if the model test with rotated or translated images, they may face some difficulties to make good performance accuracy. To resolve these challenges of hand gesture recognition,… More >

Mahmoud Ragab^1,2,3,*, Jaber Alyami^4,5

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2309-2322, 2023, DOI:10.32604/csse.2023.026877 - 01 August 2022

Abstract Liver cancer is one of the major diseases with increased mortality in recent years, across the globe. Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages. Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring. Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner. With this motivation, the… More >

Antonitta Eileen Pious^1,*, U. K. Sridevi²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2715-2726, 2023, DOI:10.32604/csse.2023.026582 - 01 August 2022

Abstract Segmentation has been an effective step that needs to be done before the classification or detection of an anomaly like Alzheimer’s on a brain scan. Segmentation helps detect pixels of the same intensity or volume and group them together as one class or region, where in that particular region of interest (ROI) can be concentrated on, rather than focusing on the entire image. In this paper White Matter Hyperintensities (WMH) is taken as a strong biomarker that supports and determines the presence of Alzheimer’s. As the first step a proper segmentation of the lesions has… More >

V. Thanikachalam^1,*, M. G. Kavitha², V. Sivamurugan¹

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2129-2145, 2023, DOI:10.32604/csse.2023.026527 - 01 August 2022

Abstract In recent times, Internet of Things (IoT) and Deep Learning (DL) models have revolutionized the diagnostic procedures of Diabetic Retinopathy (DR) in its early stages that can save the patient from vision loss. At the same time, the recent advancements made in Machine Learning (ML) and DL models help in developing Computer Aided Diagnosis (CAD) models for DR recognition and grading. In this background, the current research works designs and develops an IoT-enabled Effective Neutrosophic based Segmentation with Optimal Deep Belief Network (ODBN) model i.e., NS-ODBN model for diagnosis of DR. The presented model involves… More >

A. Deepak Kumar^1,2,*, T. Sasipraba¹

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2273-2288, 2023, DOI:10.32604/iasc.2023.028996 - 19 July 2022

Abstract Diabetic Retinopathy is a disease, which happens due to abnormal growth of blood vessels that causes spots on the vision and vision loss. Various techniques are applied to identify the disease in the early stage with different methods and parameters. Machine Learning (ML) techniques are used for analyzing the images and finding out the location of the disease. The restriction of the ML is a dataset size, which is used for model evaluation. This problem has been overcome by using an augmentation method by generating larger datasets with multidimensional features. Existing models are using only More >

Romany F. Mansour^1,*, Eatedal Alabdulkreem²

Computer Systems Science and Engineering, Vol.44, No.2, pp. 1161-1169, 2023, DOI:10.32604/csse.2023.023307 - 15 June 2022

Abstract The analysis of remote sensing image areas is needed for climate detection and management, especially for monitoring flood disasters in critical environments and applications. Satellites are mostly used to detect disasters on Earth, and they have advantages in capturing Earth images. Using the control technique, Earth images can be used to obtain detailed terrain information. Since the acquisition of satellite and aerial imagery, this system has been able to detect floods, and with increasing convenience, flood detection has become more desirable in the last few years. In this paper, a Big Data Set-based Progressive Image More >