Fathe Jeribi^1,2, Ayesha Siddiqa^3,*, Hareem Kibriya⁴, Ali Tahir¹, Nadim Rana¹

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 955-982, 2025, DOI:10.32604/cmc.2025.065853 - 29 August 2025

Abstract Wound classification is a critical task in healthcare, requiring accurate and efficient diagnostic tools to support clinicians. In this paper, we investigated the effectiveness of the YOLO11n model in classifying different types of wound images. This study presents the training and evaluation of a lightweight YOLO11n model for automated wound classification using the AZH dataset, which includes six wound classes: Background (BG), Normal Skin (N), Diabetic (D), Pressure (P), Surgical (S), and Venous (V). The model’s architecture, optimized through experiments with varying batch sizes and epochs, ensures efficient deployment in resource-constrained environments. The model’s architecture… More >

Louyi Jiang^1,#, Sulei Wang^1,#, Jiang Xie¹, Haiya Wang², Wei Shao^3,*

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 >

Asma Aldrees¹, Hong Min^2,*, 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 >

Tanzila Saba¹, Muhammad Mujahid¹, Shaha Al-Otaibi², Noor Ayesha³, Amjad Rehman Khan^1,*

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 >

Mohd Anjum¹, Naoufel Kraiem², Hong Min^3,*, 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 >

Yuhang Wang^1,*, 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 >

Muwei Jian^1,2,#,*, Ronghua Wu^1,#, 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 >

Ushaa Eswaran^1,*, 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 >

Safdar Ali¹, Saad Asad¹, Zeeshan Asghar¹, Atif Ali¹, Dohyeun Kim^2,*

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 >

A. Soujanya^1,*, 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 >