Hazem Farah¹, Akram Bennour^1,*, Hama Soltani¹, Mouaaz Nahas², Rashiq Rafiq Marie³, Mohammed Al-Sarem^3,4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3335-3348, 2025, DOI:10.32604/cmc.2025.067226 - 23 September 2025

Abstract This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an… More >

Shaoyong Hong¹, Bo Yang², Yan Chen², Hao Quan³, Shan Liu⁴, Minyi Tang^5,*, Jiawei Tian^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1091-1112, 2025, DOI:10.32604/cmes.2025.066165 - 31 July 2025

Abstract 3D medical image reconstruction has significantly enhanced diagnostic accuracy, yet the reliance on densely sampled projection data remains a major limitation in clinical practice. Sparse-angle X-ray imaging, though safer and faster, poses challenges for accurate volumetric reconstruction due to limited spatial information. This study proposes a 3D reconstruction neural network based on adaptive weight fusion (AdapFusionNet) to achieve high-quality 3D medical image reconstruction from sparse-angle X-ray images. To address the issue of spatial inconsistency in multi-angle image reconstruction, an innovative adaptive fusion module was designed to score initial reconstruction results during the inference stage and… More >

A. Chirita^a,*, A. Hustuc^b, N. Nasedchina^a, S. Vatavu^a

Chalcogenide Letters, Vol.20, No.11, pp. 803-809, 2023, DOI:10.15251/CL.2023.2011.803

Abstract The polymer/67at %(As₂S₃)_0.985(Bi₂Se₃)_0.015:33 at.% As₂Se₃ structure for X-ray imaging has been investigated. The possibility of registering relief-phase images for radiation of “white” spectrum of tungsten anode X-ray tube was shown. More >

Ha Manh Toan¹, Lam Thanh Hien², Ngo Duc Vinh³, Do Nang Toan^1,*

CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5001-5016, 2023, DOI:10.32604/cmc.2023.033429 - 28 December 2022

Abstract Deep learning created a sharp rise in the development of autonomous image recognition systems, especially in the case of the medical field. Among lung problems, tuberculosis, caused by a bacterium called Mycobacterium tuberculosis, is a dangerous disease because of its infection and damage. When an infected person coughs or sneezes, tiny droplets can bring pathogens to others through inhaling. Tuberculosis mainly damages the lungs, but it also affects any part of the body. Moreover, during the period of the COVID-19 (coronavirus disease 2019) pandemic, the access to tuberculosis diagnosis and treatment has become more difficult, so… More >

Jahangir Jabbar¹, Muzammil Hussain², Hassaan Malik^2,*, Abdullah Gani³, Ali Haider Khan², Muhammad Shiraz⁴

CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 1827-1844, 2022, DOI:10.32604/cmc.2022.024965 - 18 May 2022

Abstract Wrist cracks are the most common sort of cracks with an excessive occurrence rate. For the routine detection of wrist cracks, conventional radiography (X-ray medical imaging) is used but periodically issues are presented by crack depiction. Wrist cracks often appear in the human arbitrary bone due to accidental injuries such as slipping. Indeed, many hospitals lack experienced clinicians to diagnose wrist cracks. Therefore, an automated system is required to reduce the burden on clinicians and identify cracks. In this study, we have designed a novel residual network-based convolutional neural network (CNN) for the crack detection… More >

Chung Le Van¹, Vikram Puri¹, Nguyen Thanh Thao², Dac-Nhuong Le^3,4,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 17-33, 2021, DOI:10.32604/cmc.2020.013125 - 30 October 2020

Abstract With the rapid growth of the autonomous system, deep learning has become integral parts to enumerate applications especially in the case of healthcare systems. Human body vertebrae are the longest and complex parts of the human body. There are numerous kinds of conditions such as scoliosis, vertebra degeneration, and vertebrate disc spacing that are related to the human body vertebrae or spine or backbone. Early detection of these problems is very important otherwise patients will suffer from a disease for a lifetime. In this proposed system, we developed an autonomous system that detects lumbar implants More >

Daniel C. Moura¹, Jorge G. Barbosa¹, Ana M. Reis², João Manuel R. S. Tavares³

CMES-Computer Modeling in Engineering & Sciences, Vol.60, No.2, pp. 115-138, 2010, DOI:10.3970/cmes.2010.060.115

Abstract This paper presents a new method for the calibration of biplanar radiography that makes possible performing 3D reconstructions of the spine using conventional radiological systems. A novel approach is proposed in which a measuring device is used for determining focal distance and have a rough estimation of translation parameters. Using these data, 3D reconstructions of the spine with correct scale were successfully obtained without the need of calibration objects, something that was not previously achieved. For superior results, two optional steps may be executed that involve an optimisation of the geometrical parameters, followed by a… More >