Ghada Atteia¹, Abdulaziz Altamimi², Nihal Abuzinadah³, Khaled Alnowaiser⁴, Muhammad Umer^5,*, Yunyoung Nam⁶, Yongwon Cho^6,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.1, 2026, DOI:10.32604/cmes.2026.078510 - 27 April 2026

Abstract Cardiovascular diseases (CVD) remain the leading cause of global mortality, making early and accurate diagnosis essential for improving patient outcomes. However, most existing deep learning approaches address cardiac image segmentation or disease classification independently, limiting their effectiveness in complex clinical decision-making scenarios. In this study, we propose an explainable spatio-temporal deep learning framework that integrates segmentation-guided representation learning with efficient temporal modeling for automated CVD detection. The proposed architecture incorporates the Segment Anything Model for Medical Imaging in 2D (SAM-Med2D) to achieve accurate cardiac structure segmentation, followed by Mamba-based temporal feature extraction and Transformer-driven spatial… More >

Kadriye Simsek Alan^*, Busra Senel Kahyaoglu

Journal on Artificial Intelligence, Vol.8, pp. 203-230, 2026, DOI:10.32604/jai.2026.078359 - 15 April 2026

Abstract Cardiovascular diseases remain one of the leading causes of mortality worldwide, making early and reliable diagnosis a critical challenge for modern healthcare systems. In this study, a systematic comparative performance analysis of widely used machine learning algorithms is conducted for the early detection of heart disease using tabular clinical data. Rather than proposing a novel model architecture, the primary objective is to provide a fair, reproducible, and clinically meaningful evaluation of commonly adopted classifiers under consistent experimental conditions. The Kaggle Heart Failure dataset is employed, and multiple machine learning models—including tuned Random Forest, tuned XGBoost,… More >

Sang-Hyun Lee^*, Qingtao Meng

CMC-Computers, Materials & Continua, Vol.87, No.3, 2026, DOI:10.32604/cmc.2026.077207 - 09 April 2026

Abstract Accurate and timely detection of rice leaf diseases is critical for ensuring global food security and maximizing agricultural yields. However, existing deep learning methods often struggle to balance the high accuracy required for detecting multi-scale lesions in complex field environments with the computational efficiency necessary for edge device deployment. This paper proposes You Only Look Once for Lightweight Detection (YOLOv11-LD), a lightweight object detection model for multi-scale rice leaf disease detection in real paddy field environments. The model is built on YOLOv11n and integrates a Re-parameterized Vision Transformer (RepViT) backbone, a Bidirectional Feature Pyramid Network… More >

Sara Tehsin^1,*, Muhammad John Abbas², Inzamam Mashood Nasir¹, Fadwa Alrowais³, Reham Abualhamayel⁴, Abdulsamad Ebrahim Yahya⁵, Radwa Marzouk⁶

CMC-Computers, Materials & Continua, Vol.87, No.2, 2026, DOI:10.32604/cmc.2026.076515 - 12 March 2026

Abstract Globally, diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness. To treat these vision disorders effectively, proper diagnosis must occur in a timely manner, and with conventional methods such as fundus photography, optical coherence tomography (OCT), and slit-lamp imaging, much depends on an expert’s interpretation of the images, making the systems very labor-intensive to operate. Moreover, clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices. To solve these problems, we have developed the Efficient Channel-Spatial Attention Network (ECSA-Net), a new deep learning-based… More >

Meng Wang¹, Jinghan Cai¹, Wenzheng Liu¹, Xue Yang¹, Jingjing Zhang¹, Qiangmin Zhou¹, Fanzhen Wang¹, Hang Zhang^1,*, Tonghai Liu^2,*

Phyton-International Journal of Experimental Botany, Vol.95, No.1, 2026, DOI:10.32604/phyton.2025.075541 - 30 January 2026

Abstract Tomato is a major economic crop worldwide, and diseases on tomato leaves can significantly reduce both yield and quality. Traditional manual inspection is inefficient and highly subjective, making it difficult to meet the requirements of early disease identification in complex natural environments. To address this issue, this study proposes an improved YOLO11-based model, YOLO-SPDNet (Scale Sequence Fusion, Position-Channel Attention, and Dual Enhancement Network). The model integrates the SEAM (Self-Ensembling Attention Mechanism) semantic enhancement module, the MLCA (Mixed Local Channel Attention) lightweight attention mechanism, and the SPA (Scale-Position-Detail Awareness) module composed of SSFF (Scale Sequence Feature… More >

Vaskar Chakma^1,*, Md Jaheid Hasan Nerab¹, Abdur Rouf¹, Abu Sayed², Hossem Md Saim³, Md. Nournabi Khan³

Journal of Intelligent Medicine and Healthcare, Vol.4, pp. 1-35, 2026, DOI:10.32604/jimh.2026.074347 - 23 January 2026

Abstract Smoking continues to be a major preventable cause of death worldwide, affecting millions through damage to the heart, metabolism, liver, and kidneys. However, current medical screening methods often miss the early warning signs of smoking-related health problems, leading to late-stage diagnoses when treatment options become limited. This study presents a systematic comparative evaluation of machine learning approaches for smoking-related health risk assessment, emphasizing clinical interpretability and practical deployment over algorithmic innovation. We analyzed health screening data from 55,691 individuals, examining various health indicators including body measurements, blood tests, and demographic information. We tested three advanced… More >

Wen-Tsai Sung¹, Indra Griha Tofik Isa^2,3, Sung-Jung Hsiao^4,*

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-31, 2026, DOI:10.32604/cmc.2025.070922 - 09 December 2025

Abstract Mango is a plant with high economic value in the agricultural industry; thus, it is necessary to maximize the productivity performance of the mango plant, which can be done by implementing artificial intelligence. In this study, a lightweight object detection model will be developed that can detect mango plant conditions based on disease potential, so that it becomes an early detection warning system that has an impact on increasing agricultural productivity. The proposed lightweight model integrates YOLOv7-Tiny and the proposed modules, namely the C2S module. The C2S module consists of three sub-modules such as the… More >

Qingtao Meng, Sang-Hyun Lee^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.069970 - 10 November 2025

Abstract This study proposes a lightweight rice disease detection model optimized for edge computing environments. The goal is to enhance the You Only Look Once (YOLO) v5 architecture to achieve a balance between real-time diagnostic performance and computational efficiency. To this end, a total of 3234 high-resolution images (2400 × 1080) were collected from three major rice diseases Rice Blast, Bacterial Blight, and Brown Spot—frequently found in actual rice cultivation fields. These images served as the training dataset. The proposed YOLOv5-V2 model removes the Focus layer from the original YOLOv5s and integrates ShuffleNet V2 into the… More >

Kwok Tai Chui^1,*, Varsha Arya¹, Brij B. Gupta^2,3,4,*, Miguel Torres-Ruiz⁵, Razaz Waheeb Attar⁶

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-23, 2026, DOI:10.32604/cmc.2025.068842 - 10 November 2025

Abstract Parkinson’s disease (PD) is a debilitating neurological disorder affecting over 10 million people worldwide. PD classification models using voice signals as input are common in the literature. It is believed that using deep learning algorithms further enhances performance; nevertheless, it is challenging due to the nature of small-scale and imbalanced PD datasets. This paper proposed a convolutional neural network-based deep support vector machine (CNN-DSVM) to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets. A customized kernel function reduces the impact… 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 >