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 >

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 >

Nameer Baht^1,*, Enrique Domínguez^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4441-4455, 2025, DOI:10.32604/cmc.2025.069528 - 23 October 2025

Abstract Coffee is an important agricultural commodity, and its production is threatened by various diseases. It is also a source of concern for coffee-exporting countries, which is causing them to rethink their strategies for the future. Maintaining crop production requires early diagnosis. Notably, Coffee Leaf Miner (CLM) Machine learning (ML) offers promising tools for automated disease detection. Early detection of CLM is crucial for minimising yield losses. However, this study explores the effectiveness of using Convolutional Neural Networks (CNNs) with transfer learning algorithms ResNet50, DenseNet121, MobileNet, Inception, and hybrid VGG19 for classifying coffee leaf images as… More >

Kunal Thakur¹, Ashu Taneja^1,*, Ali Alqahtani², Nayef Alqahtani³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4827-4848, 2025, DOI:10.32604/cmc.2025.067901 - 23 October 2025

Abstract Accident detection plays a critical role in improving traffic safety by enabling timely emergency response and reducing the impact of road incidents. The main challenge lies in achieving real-time, reliable and highly accurate detection across diverse Internet-of-vehicles (IoV) environments. To overcome this challenge, this paper leverages deep learning to automatically learn patterns from visual data to detect accidents with high accuracy. A visual classification model based on the ResNet-50 architecture is presented for distinguishing between accident and non-accident images. The model is trained and tested on a labeled dataset and achieves an overall accuracy of… More >

Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025

Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

Nayeemul Islam Nayeem¹, Shirin Mahbuba¹, Sanjida Islam Disha¹, Md Rifat Hossain Buiyan¹, Shakila Rahman^1,*, M. Abdullah-Al-Wadud², Jia Uddin^3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1541-1557, 2025, DOI:10.32604/cmc.2025.065061 - 29 August 2025

Abstract Human activity recognition is a significant area of research in artificial intelligence for surveillance, healthcare, sports, and human-computer interaction applications. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The article benchmarks the performance of You Only Look Once version 11-based (YOLOv11-based) architecture for multi-class human activity recognition. The dataset consists of 14,186 images across 19 activity classes, from dynamic activities such as running and swimming to static activities such as sitting and sleeping. Preprocessing included resizing all images to 512 512 pixels, annotating them… More >

Roseline Oluwaseun Ogundokun^*, Pius Adewale Owolawi, Chunling Tu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4869-4885, 2025, DOI:10.32604/cmc.2025.064944 - 30 July 2025

Abstract Breast cancer is among the leading causes of cancer mortality globally, and its diagnosis through histopathological image analysis is often prone to inter-observer variability and misclassification. Existing machine learning (ML) methods struggle with intra-class heterogeneity and inter-class similarity, necessitating more robust classification models. This study presents an ML classifier ensemble hybrid model for deep feature extraction with deep learning (DL) and Bat Swarm Optimization (BSO) hyperparameter optimization to improve breast cancer histopathology (BCH) image classification. A dataset of 804 Hematoxylin and Eosin (H&E) stained images classified as Benign, in situ, Invasive, and Normal categories (ICIAR2018_BACH_Challenge) has… More >

Xiaofeng Liu^*, Yubin Bao, Fangling Leng

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3503-3517, 2025, DOI:10.32604/cmc.2025.064544 - 03 July 2025

Abstract The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search (NAS) algorithms designed to optimize neural network structures. However, these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance. Traditional NAS approaches, which rely on exhaustive evaluations and large training datasets, are inefficient for solving complex image classification tasks within limited time frames. To address these challenges, this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models, specifically using supernet to… More >

Min Ji^*, Shucheng Yang

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3075-3103, 2025, DOI:10.32604/cmc.2025.064166 - 03 July 2025

Abstract In agricultural production, crop images are commonly used for the classification and identification of various crops. However, several challenges arise, including low image clarity, elevated noise levels, low accuracy, and poor robustness of existing classification models. To address these issues, this research proposes an innovative crop image classification model named Lap-FEHRNet, which integrates a Laplacian Pyramid Super Resolution Network (LapSRN) with a feature enhancement high-resolution network based on attention mechanisms (FEHRNet). To mitigate noise interference, this research incorporates the LapSRN network, which utilizes a Laplacian pyramid structure to extract multi-level feature details from low-resolution images… More >

Honglin Wang¹, Yaohua Xu^2,*, Cheng Zhu³

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4999-5018, 2025, DOI:10.32604/cmc.2025.061848 - 19 May 2025

Abstract Medical image classification is crucial in disease diagnosis, treatment planning, and clinical decision-making. We introduced a novel medical image classification approach that integrates Bayesian Random Semantic Data Augmentation (BSDA) with a Vision Mamba-based model for medical image classification (MedMamba), enhanced by residual connection blocks, we named the model BSDA-Mamba. BSDA augments medical image data semantically, enhancing the model’s generalization ability and classification performance. MedMamba, a deep learning-based state space model, excels in capturing long-range dependencies in medical images. By incorporating residual connections, BSDA-Mamba further improves feature extraction capabilities. Through comprehensive experiments on eight medical image More >