Ghadah Naif Alwakid¹, Samabia Tehsin^2,*, Mamoona Humayun^3,*, Asad Farooq², Ibrahim Alrashdi¹, Amjad Alsirhani¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-21, 2026, DOI:10.32604/cmc.2025.069162 - 10 November 2025

Abstract Skin diseases affect millions worldwide. Early detection is key to preventing disfigurement, lifelong disability, or death. Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance, and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks (CNNs). We frame skin lesion recognition as graph-based reasoning and, to ensure fair evaluation and avoid data leakage, adopt a strict lesion-level partitioning strategy. Each image is first over-segmented using SLIC (Simple Linear Iterative Clustering) to produce perceptually homogeneous superpixels. These superpixels form the nodes of a region-adjacency graph whose edges encode… More >

Adeel Akram¹, Tallha Akram², Ghada Atteia^3,*, Ayman Qahmash⁴, Sultan Alanazi⁵, Faisal Mohammad Alotaibi⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2311-2337, 2025, DOI:10.32604/cmes.2025.069697 - 26 November 2025

Abstract Advancements in radiation-based imaging and computational intelligence have significantly improved medical diagnostics, particularly in dermatology. This study presents an ensemble-based skin lesion classification framework that integrates deep neural networks (DNNs) with transfer learning, a customized DNN, and an optimized self-learning binary differential evolution (SLBDE) algorithm for feature selection and fusion. Leveraging computational techniques alongside medical imaging modalities, the proposed framework extracts and fuses discriminative features from multiple pre-trained models to improve classification robustness. The methodology is evaluated on benchmark datasets, including ISIC 2017 and the Argentina Skin Lesion dataset, demonstrating superior accuracy, precision, and F1-score… More >

Mohamed Hammad^1,2,*, Mohammed ElAffendi¹, Souham Meshoul^3,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1055-1086, 2025, DOI:10.32604/cmes.2025.072650 - 30 October 2025

Abstract Skin cancer is among the most common malignancies worldwide, but its mortality burden is largely driven by aggressive subtypes such as melanoma, with outcomes varying across regions and healthcare settings. These variations emphasize the importance of reliable diagnostic technologies that support clinicians in detecting skin malignancies with higher accuracy. Traditional diagnostic methods often rely on subjective visual assessments, which can lead to misdiagnosis. This study addresses these challenges by developing HybridFusionNet, a novel model that integrates Convolutional Neural Networks (CNN) with 1D feature extraction techniques to enhance diagnostic accuracy. Utilizing two extensive datasets, BCN20000 and… More >

Jae Sung Son¹, Soo-Jin Kim^2,*

Congenital Heart Disease, Vol.20, No.4, pp. 421-440, 2025, DOI:10.32604/chd.2025.070250 - 18 September 2025

Abstract Background: Advancements in diagnostic tools, surgical techniques, and long-term management have significantly improved survival among individuals with congenital heart disease (CHD), leading to an evolving epidemiologic profile characterized by increasing procedural complexity and a growing adult CHD population. This study aimed to examine nationwide trends in CHD surgeries over a 17-year period, with a focus on temporal shifts in surgical volume, procedural complexity, and age-specific incidence. Methods: A total of 41,608 CHD surgeries and 85,417 surgical procedures performed between 2002 and 2018 were identified from a nationwide health insurance database. Temporal trends were evaluated using segmented… More >

Zhuoqun Xia¹, Hangyu Hu¹, Wenjing Li^2,3, Qisheng Jiang¹, Lan Pu¹, Yicong Shu¹, Arun Kumar Sangaiah^4,5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2683-2683, 2025, DOI:10.32604/cmes.2025.069871 - 31 August 2025

Abstract This article has no abstract. More >

Kun Lan¹, Feiyang Gao¹, Xiaoliang Jiang^1,*, Jianzhen Cheng^2,*, Simon Fong³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4805-4824, 2025, DOI:10.32604/cmc.2025.065864 - 30 July 2025

Abstract With the continuous development of artificial intelligence and machine learning techniques, there have been effective methods supporting the work of dermatologist in the field of skin cancer detection. However, object significant challenges have been presented in accurately segmenting melanomas in dermoscopic images due to the objects that could interfere human observations, such as bubbles and scales. To address these challenges, we propose a dual U-Net network framework for skin melanoma segmentation. In our proposed architecture, we introduce several innovative components that aim to enhance the performance and capabilities of the traditional U-Net. First, we establish… More >

Tallha Akram^1,*, Fahdah Almarshad¹, Anas Alsuhaibani¹, Syed Rameez Naqvi^2,3

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2333-2359, 2025, DOI:10.32604/cmes.2025.064079 - 30 May 2025

Abstract Melanoma is the deadliest form of skin cancer, with an increasing incidence over recent years. Over the past decade, researchers have recognized the potential of computer vision algorithms to aid in the early diagnosis of melanoma. As a result, a number of works have been dedicated to developing efficient machine learning models for its accurate classification; still, there remains a large window for improvement necessitating further research efforts. Limitations of the existing methods include lower accuracy and high computational complexity, which may be addressed by identifying and selecting the most discriminative features to improve classification… More >

Poonam Sharma¹, Bhisham Sharma^2,*, Dhirendra Prasad Yadav³, Surbhi Bhatia Khan^4,5,6,*, Ahlam Almusharraf⁷

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3227-3245, 2025, DOI:10.32604/cmc.2025.062376 - 16 April 2025

Abstract Rapid and precise diagnostic tools for Monkeypox (Mpox) lesions are crucial for effective treatment because their symptoms are similar to those of other pox-related illnesses, like smallpox and chickenpox. The morphological similarities between smallpox, chickenpox, and monkeypox, particularly in how they appear as rashes and skin lesions, which can sometimes make diagnosis challenging. Chickenpox lesions appear in many simultaneous phases and are more diffuse, often beginning on the trunk. In contrast, monkeypox lesions emerge progressively and are typically centralized on the face, palms, and soles. To provide accessible diagnostics, this study introduces a novel method… More >

Tariq Mahmood^1,2, Tanzila Saba¹, Faten S. Alamri^3,*, Alishba Tahir⁴, Noor Ayesha⁵

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1173-1193, 2025, DOI:10.32604/cmc.2025.061150 - 26 March 2025

Abstract Innovation in learning algorithms has made retinal vessel segmentation and automatic grading techniques crucial for clinical diagnosis and prevention of diabetic retinopathy. The traditional methods struggle with accuracy and reliability due to multi-scale variations in retinal blood vessels and the complex pathological relationship in fundus images associated with diabetic retinopathy. While the single-modal diabetic retinopathy grading network addresses class imbalance challenges and lesion representation in fundus image data, dual-modal diabetic retinopathy grading methods offer superior performance. However, the scarcity of dual-modal data and the lack of effective feature fusion methods limit their potential due to… More >

Armughan Ali^1,2, Hooria Shahbaz², Robertas Damaševičius^3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1367-1398, 2025, DOI:10.32604/cmc.2025.059301 - 26 March 2025

Abstract Skin cancer is the most prevalent cancer globally, primarily due to extensive exposure to Ultraviolet (UV) radiation. Early identification of skin cancer enhances the likelihood of effective treatment, as delays may lead to severe tumor advancement. This study proposes a novel hybrid deep learning strategy to address the complex issue of skin cancer diagnosis, with an architecture that integrates a Vision Transformer, a bespoke convolutional neural network (CNN), and an Xception module. They were evaluated using two benchmark datasets, HAM10000 and Skin Cancer ISIC. On the HAM10000, the model achieves a precision of 95.46%, an… More >