Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Khushmeen Kaur Brar¹, Bhawna Goyal¹, Ayush Dogra², Sampangi Rama Reddy³, Ahmed Alkhayyat⁴, Rajesh Singh⁵, Manob Jyoti Saikia^6,7,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4217-4263, 2024, DOI:10.32604/cmc.2024.052548 - 19 December 2024

Abstract Skin cancer has been recognized as one of the most lethal and complex types of cancer for over a decade. The diagnosis of skin cancer is of paramount importance, yet the process is intricate and challenging. The analysis and modeling of human skin pose significant difficulties due to its asymmetrical nature, the visibility of dense hair, and the presence of various substitute characteristics. The texture of the epidermis is notably different from that of normal skin, and these differences are often evident in cases of unhealthy skin. As a consequence, the development of an effective… More >

Miao Huang¹, Maedeh Lotfi¹, Heyang Wang⁴, Hayley Sussman⁵, Kevin Connell¹, Quang Vo¹, Malisa Sarntinoranont¹, Hitomi Yamaguchi¹, Juan Guan², Xin Tang^1,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.011130

Abstract Mechanotransduction, i.e., living cells sense and transduce mechanical forces into intracellular biochemical signaling and gene expression, is ubiquitous across diverse organisms. Increasing evidence suggests that mechanotransduction significantly influences cell functions and its mis-regulation is at the heart of various pathologies. A quantitative characterization of the relationship between mechanical forces and resulted mechanotransduction is pivotal in understanding the rules of life and innovating new therapeutic strategies [1-3]. However, while such relationship on the cell surface membrane and cytoskeleton have been well studied, little is known about whether/how mechanical forces applied on the cell interior nucleus (“headquarter… More >

Chenyu Liang¹, Bo Zeng², Mai Tanaka³, Andrea Kannita Noy¹, Matthew Barrett¹, Erica Hengartner¹, Abygale Cochrane⁴, Laura Garzon¹, Mitchell Litvinov⁵, Dietmar Siemann³, Xin Tang^1,3,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.011126

Abstract Cancer accounts for 12.6% of all human deaths worldwide and 90% of cancer-related deaths are due to metastasis: the dissemination of invasive tumor cells from the primary tumors to other vital organs [1-3]. However, how these invasive tumor cells coordinate with each other to achieve the dissemination remains unclear. Recently we discovered that human tumor cells can initiate and transmit previously unknown long-distance (~100s m) intercellular biochemical waves in a microenvironment-mechanics-regulated manner. [4-5] In this presentation, we will present our new results on (1) the 2D/3D spatial-temporal characterization of the long-distance and the intra-/inter-cellular Ca²⁺ signals; More >

Yen-Jen Lai¹, I-Ling Chang^1,*

The International Conference on Computational & Experimental Engineering and Sciences, Vol.31, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.012918

Abstract Biomechanical research reveals that the geometric shapes and dynamic behaviors of organ tissues play a pivotal role in determining their mechanical properties. Recent advancements in time-correlated imaging technologies, such as Computed Tomography (4D-CT) and Magnetic Resonance Imaging (4D-MRI), have enabled the non-invasive capture of both geometric data and dynamic information over time. However, the manual segmentation of these extensive datasets proves to be laborious and expensive. This study introduces an automated workflow designed for image segmentation and classification within 4D-CT scans, with a specific focus on the bone structures surrounding the Trapeziometacarpal (TMC) joint in More >

Mohammed A. Mahdi¹, Shahanawaj Ahamad², Sawsan A. Saad³, Alaa Dafhalla³, Alawi Alqushaibi⁴, Rizwan Qureshi^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2351-2373, 2024, DOI:10.32604/cmes.2024.055723 - 31 October 2024

Abstract The segmentation of head and neck (H&N) tumors in dual Positron Emission Tomography/Computed Tomography (PET/CT) imaging is a critical task in medical imaging, providing essential information for diagnosis, treatment planning, and outcome prediction. Motivated by the need for more accurate and robust segmentation methods, this study addresses key research gaps in the application of deep learning techniques to multimodal medical images. Specifically, it investigates the limitations of existing 2D and 3D models in capturing complex tumor structures and proposes an innovative 2.5D UNet Transformer model as a solution. The primary research questions guiding this study… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.11, pp. 1829-1829, 2024, DOI:10.32604/or.2024.056904 - 16 October 2024

Abstract This article has no abstract. More >

Mahesh Thyluru Ramakrishna¹, Kuppusamy Pothanaicker², Padma Selvaraj³, Surbhi Bhatia Khan^4,7,*, Vinoth Kumar Venkatesan⁵, Saeed Alzahrani⁶, Mohammad Alojail⁶

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 867-883, 2024, DOI:10.32604/cmc.2024.053563 - 15 October 2024

Abstract Brain tumor is a global issue due to which several people suffer, and its early diagnosis can help in the treatment in a more efficient manner. Identifying different types of brain tumors, including gliomas, meningiomas, pituitary tumors, as well as confirming the absence of tumors, poses a significant challenge using MRI images. Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification. These methods often rely on manual feature extraction and basic convolutional neural networks (CNNs). The limitations include inadequate accuracy, poor generalization of new data, and limited ability… More >

Lixia Yang^1,*, Danqi Huang¹, Zongxin Liu², Lei Zhao¹, Xuejing Shen¹, Haizhou Wang¹

The International Conference on Computational & Experimental Engineering and Sciences, Vol.29, No.4, pp. 1-2, 2024, DOI:10.32604/icces.2024.012690

Abstract Neutrons possess unique advantages in the study of element and component distribution, internal damage defects, crystal structure, and multi-scale stress field evolution of engineering materials due to their strong penetrating ability, sensitivity to light elements, and non-destructive properties. This study introduces the application of neutron diffraction technology for characterizing residual stress in full-size high-speed iron wheels and neutron imaging technology for three-dimensional characterization of hydrogen distribution in titanium alloys treated with hot hydrogen.
Residual stress plays a critical role in the design, manufacturing, assembly, and service life cycle of wheel structures. It is a significant factor… More >

Deema Mohammed Alsekait¹, Ahmed Younes Shdefat², Ayman Nabil³, Asif Nawaz^4,*, Muhammad Rizwan Rashid Rana⁴, Zohair Ahmed⁵, Hanaa Fathi⁶, Diaa Salama AbdElminaam^6,7,8

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3967-3990, 2024, DOI:10.32604/cmc.2024.054591 - 12 September 2024

Abstract Heart disease remains a leading cause of morbidity and mortality worldwide, highlighting the need for improved diagnostic methods. Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults, which can reduce accuracy, especially with poor-quality images. Additionally, these methods often require significant time and expertise, making them less accessible in resource-limited settings. Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision, ultimately improving patient outcomes and reducing healthcare costs. This study introduces Heart-Net, a multi-modal deep learning framework designed to… More >