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 >

Tao Zhou^1,2, Yaxing Wang^1,2,*, Huiling Lu³, Wenwen Chai^1,2, Yunfeng Pan^1,2, Zhe Zhang^1,2

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4815-4834, 2025, DOI:10.32604/cmc.2025.062400 - 19 May 2025

Abstract The instance segmentation of impacted teeth in the oral panoramic X-ray images is hotly researched. However, due to the complex structure, low contrast, and complex background of teeth in panoramic X-ray images, the task of instance segmentation is technically tricky. In this study, the contrast between impacted Teeth and periodontal tissues such as gingiva, periodontal membrane, and alveolar bone is low, resulting in fuzzy boundaries of impacted teeth. A model based on Teeth YOLACT is proposed to provide a more efficient and accurate solution for the segmentation of impacted teeth in oral panoramic X-ray films.… More >

Fahad Ahmad^1,2,*, Saad Awadh Alanazi³, Kashaf Junaid⁴, Maryam Shabbir⁵, Asim Ali¹

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 381-405, 2025, DOI:10.32604/cmc.2025.062452 - 26 March 2025

Abstract Image processing plays a vital role in various fields such as autonomous systems, healthcare, and cataloging, especially when integrated with deep learning (DL). It is crucial in medical diagnostics, including the early detection of diseases like chronic obstructive pulmonary disease (COPD), which claimed 3.2 million lives in 2015. COPD, a life-threatening condition often caused by prolonged exposure to lung irritants and smoking, progresses through stages. Early diagnosis through image processing can significantly improve survival rates. COPD encompasses chronic bronchitis (CB) and emphysema; CB particularly increases in smokers and generally affects individuals between 50 and 70… More >

Tao Zhou^1,3, Yujie Guo^1,3,*, Caiyue Peng^1,3, Yuxia Niu^1,3, Yunfeng Pan^1,3, Huiling Lu²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4863-4882, 2024, DOI:10.32604/cmc.2024.050767 - 20 June 2024

Abstract Computer-aided diagnosis of pneumonia based on deep learning is a research hotspot. However, there are some problems that the features of different sizes and different directions are not sufficient when extracting the features in lung X-ray images. A pneumonia classification model based on multi-scale directional feature enhancement MSD-Net is proposed in this paper. The main innovations are as follows: Firstly, the Multi-scale Residual Feature Extraction Module (MRFEM) is designed to effectively extract multi-scale features. The MRFEM uses dilated convolutions with different expansion rates to increase the receptive field and extract multi-scale features effectively. Secondly, the… More >

Nguyen Trong Vinh¹, Lam Thanh Hien¹, Ha Manh Toan², Ngo Duc Vinh³, Do Nang Toan^2,*

Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 281-299, 2024, DOI:10.32604/iasc.2024.045297 - 21 May 2024

Abstract Tuberculosis is a dangerous disease to human life, and we need a lot of attempts to stop and reverse it. Significantly, in the COVID-19 pandemic, access to medical services for tuberculosis has become very difficult. The late detection of tuberculosis could lead to danger to patient health, even death. Vietnam is one of the countries heavily affected by the COVID-19 pandemic, and many residential areas as well as hospitals have to be isolated for a long time. Reality demands a fast and effective tuberculosis diagnosis solution to deal with the difficulty of accessing medical services,… More >

Fayez Alfayez^*

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1539-1560, 2024, DOI:10.32604/cmc.2024.046443 - 25 April 2024

Abstract This paper emphasizes a faster digital processing time while presenting an accurate method for identifying spine fractures in X-ray pictures. The study focuses on efficiency by utilizing many methods that include picture segmentation, feature reduction, and image classification. Two important elements are investigated to reduce the classification time: Using feature reduction software and leveraging the capabilities of sophisticated digital processing hardware. The researchers use different algorithms for picture enhancement, including the Wiener and Kalman filters, and they look into two background correction techniques. The article presents a technique for extracting textural features and evaluates three… More >

Nasser Alalwan^1,*, Ahmed I. Taloba², Amr Abozeid³, Ahmed Ibrahim Alzahrani¹, Ali H. Al-Bayatti⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2497-2517, 2024, DOI:10.32604/cmes.2023.029910 - 15 December 2023

Abstract An illness known as pneumonia causes inflammation in the lungs. Since there is so much information available from various X-ray images, diagnosing pneumonia has typically proven challenging. To improve image quality and speed up the diagnosis of pneumonia, numerous approaches have been devised. To date, several methods have been employed to identify pneumonia. The Convolutional Neural Network (CNN) has achieved outstanding success in identifying and diagnosing diseases in the fields of medicine and radiology. However, these methods are complex, inefficient, and imprecise to analyze a big number of datasets. In this paper, a new hybrid… More >

Shaik Mahaboob Basha^1,*, Victor Hugo C. de Albuquerque², Samia Allaoua Chelloug^3,*, Mohamed Abd Elaziz^4,5,6,7, Shaik Hashmitha Mohisin⁸, Suhail Parvaze Pathan⁹

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1981-2004, 2024, DOI:10.32604/cmes.2023.031425 - 17 November 2023

Abstract Manual investigation of chest radiography (CXR) images by physicians is crucial for effective decision-making in COVID-19 diagnosis. However, the high demand during the pandemic necessitates auxiliary help through image analysis and machine learning techniques. This study presents a multi-threshold-based segmentation technique to probe high pixel intensity regions in CXR images of various pathologies, including normal cases. Texture information is extracted using gray co-occurrence matrix (GLCM)-based features, while vessel-like features are obtained using Frangi, Sato, and Meijering filters. Machine learning models employing Decision Tree (DT) and Random Forest (RF) approaches are designed to categorize CXR images… More > Graphic Abstract

Abdul Haseeb¹, Muhammad Attique Khan^1,*, Faheem Shehzad¹, Majed Alhaisoni², Junaid Ali Khan¹, Taerang Kim³, Jae-Hyuk Cha³

Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529 - 28 July 2023

Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion… More >

Ahmad Almadhor^1,*, Arfat Ahmad Khan², Chitapong Wechtaisong^3,*, Iqra Yousaf⁴, Natalia Kryvinska⁵, Usman Tariq⁶, Haithem Ben Chikha¹

Computer Systems Science and Engineering, Vol.47, No.2, pp. 1775-1791, 2023, DOI:10.32604/csse.2023.039007 - 28 July 2023

Abstract Pneumothorax is a thoracic condition that occurs when a person’s lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data.… More >