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  • Open Access

    ARTICLE

    Dual-Classifier Label Correction Network for Carotid Plaque Classification on Multi-Center Ultrasound Images

    Louyi Jiang1,#, Sulei Wang1,#, Jiang Xie1, Haiya Wang2, Wei Shao3,*

    CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5445-5460, 2025, DOI:10.32604/cmc.2025.061759 - 19 May 2025

    Abstract Carotid artery plaques represent a major contributor to the morbidity and mortality associated with cerebrovascular disease, and their clinical significance is largely determined by the risk linked to plaque vulnerability. Therefore, classifying plaque risk constitutes one of the most critical tasks in the clinical management of this condition. While classification models derived from individual medical centers have been extensively investigated, these single-center models often fail to generalize well to multi-center data due to variations in ultrasound images caused by differences in physician expertise and equipment. To address this limitation, a Dual-Classifier Label Correction Network model… More >

  • Open Access

    ARTICLE

    Improving Fundus Detection Precision in Diabetic Retinopathy Using Derivative-Based Deep Neural Networks

    Asma Aldrees1, Hong Min2,*, Ashit Kumar Dutta3, Yousef Ibrahim Daradkeh4, Mohd Anjum5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2487-2511, 2025, DOI:10.32604/cmes.2025.061103 - 03 March 2025

    Abstract Fundoscopic diagnosis involves assessing the proper functioning of the eye’s nerves, blood vessels, retinal health, and the impact of diabetes on the optic nerves. Fundus disorders are a major global health concern, affecting millions of people worldwide due to their widespread occurrence. Fundus photography generates machine-based eye images that assist in diagnosing and treating ocular diseases such as diabetic retinopathy. As a result, accurate fundus detection is essential for early diagnosis and effective treatment, helping to prevent severe complications and improve patient outcomes. To address this need, this article introduces a Derivative Model for Fundus… More >

  • Open Access

    ARTICLE

    Novel Feature Extractor Framework in Conjunction with Supervised Three Class-XGBoost Algorithm for Osteosarcoma Detection from Whole Slide Medical Histopathology Images

    Tanzila Saba1, Muhammad Mujahid1, Shaha Al-Otaibi2, Noor Ayesha3, Amjad Rehman Khan1,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3337-3353, 2025, DOI:10.32604/cmc.2025.060163 - 17 February 2025

    Abstract Osteosarcomas are malignant neoplasms derived from undifferentiated osteogenic mesenchymal cells. It causes severe and permanent damage to human tissue and has a high mortality rate. The condition has the capacity to occur in any bone; however, it often impacts long bones like the arms and legs. Prompt identification and prompt intervention are essential for augmenting patient longevity. However, the intricate composition and erratic placement of osteosarcoma provide difficulties for clinicians in accurately determining the scope of the afflicted area. There is a pressing requirement for developing an algorithm that can automatically detect bone tumors with… More >

  • Open Access

    ARTICLE

    Congruent Feature Selection Method to Improve the Efficacy of Machine Learning-Based Classification in Medical Image Processing

    Mohd Anjum1, Naoufel Kraiem2, Hong Min3,*, Ashit Kumar Dutta4, Yousef Ibrahim Daradkeh5

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 357-384, 2025, DOI:10.32604/cmes.2024.057889 - 17 December 2024

    Abstract Machine learning (ML) is increasingly applied for medical image processing with appropriate learning paradigms. These applications include analyzing images of various organs, such as the brain, lung, eye, etc., to identify specific flaws/diseases for diagnosis. The primary concern of ML applications is the precise selection of flexible image features for pattern detection and region classification. Most of the extracted image features are irrelevant and lead to an increase in computation time. Therefore, this article uses an analytical learning paradigm to design a Congruent Feature Selection Method to select the most relevant image features. This process… More >

  • Open Access

    ARTICLE

    Spinal Vertebral Fracture Detection and Fracture Level Assessment Based on Deep Learning

    Yuhang Wang1,*, Zhiqin He1, Qinmu Wu1, Tingsheng Lu2, Yu Tang1, Maoyun Zhu1

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1377-1398, 2024, DOI:10.32604/cmc.2024.047379 - 25 April 2024

    Abstract This paper addresses the common orthopedic trauma of spinal vertebral fractures and aims to enhance doctors’ diagnostic efficiency. Therefore, a deep-learning-based automated diagnostic system with multi-label segmentation is proposed to recognize the condition of vertebral fractures. The whole spine Computed Tomography (CT) image is segmented into the fracture, normal, and background using U-Net, and the fracture degree of each vertebra is evaluated (Genant semi-qualitative evaluation). The main work of this paper includes: First, based on the spatial configuration network (SCN) structure, U-Net is used instead of the SCN feature extraction network. The attention mechanism and… More >

  • Open Access

    ARTICLE

    Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

    Muwei Jian1,2,#,*, Ronghua Wu1,#, Hongyu Chen1, Lanqi Fu3, Chengdong Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425 - 23 April 2023

    Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of… More >

  • Open Access

    ARTICLE

    Horizontal Voting Ensemble Based Predictive Modeling System for Colon Cancer

    Ushaa Eswaran1,*, S. Anand2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1917-1928, 2023, DOI:10.32604/csse.2023.032523 - 09 February 2023

    Abstract Colon cancer is the third most commonly diagnosed cancer in the world. Most colon AdenoCArcinoma (ACA) arises from pre-existing benign polyps in the mucosa of the bowel. Thus, detecting benign at the earliest helps reduce the mortality rate. In this work, a Predictive Modeling System (PMS) is developed for the classification of colon cancer using the Horizontal Voting Ensemble (HVE) method. Identifying different patterns in microscopic images is essential to an effective classification system. A twelve-layer deep learning architecture has been developed to extract these patterns. The developed HVE algorithm can increase the system’s performance… More >

  • Open Access

    ARTICLE

    End-to-End 2D Convolutional Neural Network Architecture for Lung Nodule Identification and Abnormal Detection in Cloud

    Safdar Ali1, Saad Asad1, Zeeshan Asghar1, Atif Ali1, Dohyeun Kim2,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 461-475, 2023, DOI:10.32604/cmc.2023.035672 - 06 February 2023

    Abstract The extent of the peril associated with cancer can be perceived from the lack of treatment, ineffective early diagnosis techniques, and most importantly its fatality rate. Globally, cancer is the second leading cause of death and among over a hundred types of cancer; lung cancer is the second most common type of cancer as well as the leading cause of cancer-related deaths. Anyhow, an accurate lung cancer diagnosis in a timely manner can elevate the likelihood of survival by a noticeable margin and medical imaging is a prevalent manner of cancer diagnosis since it is… More >

  • Open Access

    ARTICLE

    Automated Skin Lesion Diagnosis and Classification Using Learning Algorithms

    A. Soujanya1,*, N. Nandhagopal2

    Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 675-687, 2023, DOI:10.32604/iasc.2023.025930 - 06 June 2022

    Abstract Due to the rising occurrence of skin cancer and inadequate clinical expertise, it is needed to design Artificial Intelligence (AI) based tools to diagnose skin cancer at an earlier stage. Since massive skin lesion datasets have existed in the literature, the AI-based Deep Learning (DL) models find useful to differentiate benign and malignant skin lesions using dermoscopic images. This study develops an Automated Seeded Growing Segmentation with Optimal EfficientNet (ARGS-OEN) technique for skin lesion segmentation and classification. The proposed ASRGS-OEN technique involves the design of an optimal EfficientNet model in which the hyper-parameter tuning process More >

  • Open Access

    ARTICLE

    Artificial Intelligence Based Prostate Cancer Classification Model Using Biomedical Images

    Areej A. Malibari1, Reem Alshahrani2, Fahd N. Al-Wesabi3,*, Siwar Ben Haj Hassine3, Mimouna Abdullah Alkhonaini4, Anwer Mustafa Hilal5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 3799-3813, 2022, DOI:10.32604/cmc.2022.026131 - 29 March 2022

    Abstract Medical image processing becomes a hot research topic in healthcare sector for effective decision making and diagnoses of diseases. Magnetic resonance imaging (MRI) is a widely utilized tool for the classification and detection of prostate cancer. Since the manual screening process of prostate cancer is difficult, automated diagnostic methods become essential. This study develops a novel Deep Learning based Prostate Cancer Classification (DTL-PSCC) model using MRI images. The presented DTL-PSCC technique encompasses EfficientNet based feature extractor for the generation of a set of feature vectors. In addition, the fuzzy k-nearest neighbour (FKNN) model is utilized More >

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