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  • Open Access

    ARTICLE

    Multi-Scale Vision Transformer with Dynamic Multi-Loss Function for Medical Image Retrieval and Classification

    Omar Alqahtani, Mohamed Ghouse*, Asfia Sabahath, Omer Bin Hussain, Arshiya Begum

    CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2221-2244, 2025, DOI:10.32604/cmc.2025.061977 - 16 April 2025

    Abstract This paper introduces a novel method for medical image retrieval and classification by integrating a multi-scale encoding mechanism with Vision Transformer (ViT) architectures and a dynamic multi-loss function. The multi-scale encoding significantly enhances the model’s ability to capture both fine-grained and global features, while the dynamic loss function adapts during training to optimize classification accuracy and retrieval performance. Our approach was evaluated on the ISIC-2018 and ChestX-ray14 datasets, yielding notable improvements. Specifically, on the ISIC-2018 dataset, our method achieves an F1-Score improvement of +4.84% compared to the standard ViT, with a precision increase of +5.46% More >

  • Open Access

    ARTICLE

    Leveraging Deep Learning for Precise Chronic Bronchitis Identification in X-Ray Modalities

    Fahad Ahmad1,2,*, 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 >

  • Open Access

    PROCEEDINGS

    Characterization on Fracture Toughness of Cermet Coating Coupling Instrumented Indentation and X-Ray Computed Tomography

    Ruizhe Huang1, Zhaoliang Qu1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.30, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011440

    Abstract The surface brittle fracture of cermet coating seriously restricts its application. Accurate evaluation of the fracture toughness of cermet coating is a prerequisite for improving its life. This paper aims to propose an accurate characterization method for fracture toughness of cermet coating. By coupling instrumented indentation and X‑ ray computed tomography, the indentation-induced fracture behaviors under various loads within WC-12%Co coatings were studied. The indentation response was correlated with the damage evolution directly observed within the coating. The impact of substrate effects on indentation-induced fracture behaviors was further studied using finite element analysis (FEA). The… More >

  • Open Access

    ARTICLE

    Pulmonary Edema and Pleural Effusion Detection Using EfficientNet-V1-B4 Architecture and AdamW Optimizer from Chest X-Rays Images

    Anas AbuKaraki1, Tawfi Alrawashdeh1, Sumaya Abusaleh1, Malek Zakarya Alksasbeh1,*, Bilal Alqudah1, Khalid Alemerien2, Hamzah Alshamaseen3

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1055-1073, 2024, DOI:10.32604/cmc.2024.051420 - 18 July 2024

    Abstract This paper presents a novel multiclass system designed to detect pleural effusion and pulmonary edema on chest X-ray images, addressing the critical need for early detection in healthcare. A new comprehensive dataset was formed by combining 28,309 samples from the ChestX-ray14, PadChest, and CheXpert databases, with 10,287, 6022, and 12,000 samples representing Pleural Effusion, Pulmonary Edema, and Normal cases, respectively. Consequently, the preprocessing step involves applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) method to boost the local contrast of the X-ray samples, then resizing the images to 380 × 380 dimensions, followed by using the data… More >

  • Open Access

    ARTICLE

    MSD-Net: Pneumonia Classification Model Based on Multi-Scale Directional Feature Enhancement

    Tao Zhou1,3, Yujie Guo1,3,*, Caiyue Peng1,3, Yuxia Niu1,3, Yunfeng Pan1,3, 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 >

  • Open Access

    ARTICLE

    Research on Enhanced Contraband Dataset ACXray Based on ETL

    Xueping Song1,*, Jianming Yang1, Shuyu Zhang1, Jicun Zhang1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4551-4572, 2024, DOI:10.32604/cmc.2024.049446 - 20 June 2024

    Abstract To address the shortage of public datasets for customs X-ray images of contraband and the difficulties in deploying trained models in engineering applications, a method has been proposed that employs the Extract-Transform-Load (ETL) approach to create an X-ray dataset of contraband items. Initially, X-ray scatter image data is collected and cleaned. Using Kafka message queues and the Elasticsearch (ES) distributed search engine, the data is transmitted in real-time to cloud servers. Subsequently, contraband data is annotated using a combination of neural networks and manual methods to improve annotation efficiency and implemented mean hash algorithm for… More >

  • Open Access

    ARTICLE

    Tuberculosis Diagnosis and Visualization with a Large Vietnamese X-Ray Image Dataset

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

    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 >

  • Open Access

    ARTICLE

    Automated Algorithms for Detecting and Classifying X-Ray Images of Spine Fractures

    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 >

  • Open Access

    ARTICLE

    Material-SAM: Adapting SAM for Material XCT

    Xuelong Wu1, Junsheng Wang1,*, Zhongyao Li1, Yisheng Miao1, Chengpeng Xue1, Yuling Lang2, Decai Kong2, Xiaoying Ma2, Haibao Qiao2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3703-3720, 2024, DOI:10.32604/cmc.2024.047027 - 26 March 2024

    Abstract X-ray Computed Tomography (XCT) enables non-destructive acquisition of the internal structure of materials, and image segmentation plays a crucial role in analyzing material XCT images. This paper proposes an image segmentation method based on the Segment Anything model (SAM). We constructed a dataset of carbide in nickel-based single crystal superalloys XCT images and preprocessed the images using median filtering, histogram equalization, and gamma correction. Subsequently, SAM was fine-tuned to adapt to the task of material XCT image segmentation, resulting in Material-SAM. We compared the performance of threshold segmentation, SAM, U-Net model, and Material-SAM. Our method More >

  • Open Access

    ARTICLE

    A Hybrid Classification and Identification of Pneumonia Using African Buffalo Optimization and CNN from Chest X-Ray Images

    Nasser Alalwan1,*, 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 >

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