Kaku Haribabu^1,*, Prasath R¹, Praveen Joe IR²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 413-448, 2025, DOI:10.32604/cmes.2025.059406 - 11 April 2025

Abstract Thyroid nodules, a common disorder in the endocrine system, require accurate segmentation in ultrasound images for effective diagnosis and treatment. However, achieving precise segmentation remains a challenge due to various factors, including scattering noise, low contrast, and limited resolution in ultrasound images. Although existing segmentation models have made progress, they still suffer from several limitations, such as high error rates, low generalizability, overfitting, limited feature learning capability, etc. To address these challenges, this paper proposes a Multi-level Relation Transformer-based U-Net (MLRT-UNet) to improve thyroid nodule segmentation. The MLRT-UNet leverages a novel Relation Transformer, which processes… More >

Bonan Yu^1,2, Taiping Mo^1,3, Qi Ma¹, Qiumei Li¹, Peng Sun^1,3,*

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 1057-1078, 2025, DOI:10.32604/cmc.2025.059946 - 26 March 2025

Abstract Super-resolution (SR) reconstruction addresses the challenge of enhancing image resolution, which is critical in domains such as medical imaging, remote sensing, and computational photography. High-quality image reconstruction is essential for enhancing visual details and improving the accuracy of subsequent tasks. Traditional methods, including interpolation techniques and basic CNNs, often fail to recover fine textures and detailed structures, particularly in complex or high-frequency regions. In this paper, we present Deep Supervised Swin Transformer U-Net (DSSTU-Net), a novel architecture designed to improve image SR by integrating Residual Swin Transformer Blocks (RSTB) and Deep Supervision (DS) mechanisms into… More >

Huiyao Dong¹, Igor Kotenko^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025

Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >

Rifat Sarker Aoyon¹, Ismail Hossain², M. Abdullah-Al-Wadud³, Jia Uddin^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2617-2640, 2025, DOI:10.32604/cmes.2025.060917 - 03 March 2025

Abstract This research introduces a unique approach to segmenting breast cancer images using a U-Net-based architecture. However, the computational demand for image processing is very high. Therefore, we have conducted this research to build a system that enables image segmentation training with low-power machines. To accomplish this, all data are divided into several segments, each being trained separately. In the case of prediction, the initial output is predicted from each trained model for an input, where the ultimate output is selected based on the pixel-wise majority voting of the expected outputs, which also ensures data privacy.… More >

Yitong Zhou, Tianliang Lu^*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3591-3608, 2025, DOI:10.32604/cmc.2024.057554 - 17 February 2025

Abstract Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US$3… More >

Lakshmi S V V¹, Shiloah Elizabeth Darmanayagam^1,*, Sunil Retmin Raj Cyril²

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 733-757, 2025, DOI:10.32604/cmes.2024.056424 - 17 December 2024

Abstract Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people everywhere. Due to its ability to produce a detailed view of the soft tissues, including the spinal cord, nerves, intervertebral discs, and vertebrae, Magnetic Resonance Imaging is thought to be the most effective method for imaging the spine. The semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar diseases. It is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of… More >

Haemin Jung¹, Heesung Park², Hae Sun Jung³, Kwangyon Lee^4,*

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2261-2279, 2024, DOI:10.32604/cmc.2024.057118 - 18 November 2024

Abstract The growing demand for energy-efficient solutions has led to increased interest in analyzing building facades, as buildings contribute significantly to energy consumption in urban environments. However, conventional image segmentation methods often struggle to capture fine details such as edges and contours, limiting their effectiveness in identifying areas prone to energy loss. To address this challenge, we propose a novel segmentation methodology that combines object-wise processing with a two-stage deep learning model, Cascade U-Net. Object-wise processing isolates components of the facade, such as walls and windows, for independent analysis, while Cascade U-Net incorporates contour information to… More >

Amjad Rehman¹, Muhammad Mujahid¹, Robertas Damasevicius^2,*, Faten S Alamri³, Tanzila Saba¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

Qianshuo Liu, Jun Zhao^*

Phyton-International Journal of Experimental Botany, Vol.93, No.10, pp. 2663-2681, 2024, DOI:10.32604/phyton.2024.056054 - 30 October 2024

Abstract Traditional machine vision algorithms have difficulty handling the interference of light and shadow changes, broken rows, and weeds in the complex growth circumstances of soybean fields, which leads to erroneous navigation route segmentation. There are additional shortcomings in the feature extractFion capabilities of the conventional U-Net network. Our suggestion is to utilize an improved U-Net-based method to tackle these difficulties. First, we use ResNet’s powerful feature extraction capabilities to replace the original U-Net encoder. To enhance the concentration on characteristics unique to soybeans, we integrate a multi-scale high-performance attention mechanism. Furthermore, to do multi-scale feature… More >

Tao Zhou^1,3, Yunfeng Pan^1,3,*, Huiling Lu², Pei Dang^1,3, Yujie Guo^1,3, Yaxing Wang^1,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4813-4832, 2024, DOI:10.32604/cmc.2024.054685 - 12 September 2024

Abstract Multimodal lung tumor medical images can provide anatomical and functional information for the same lesion. Such as Positron Emission Computed Tomography (PET), Computed Tomography (CT), and PET-CT. How to utilize the lesion anatomical and functional information effectively and improve the network segmentation performance are key questions. To solve the problem, the Saliency Feature-Guided Interactive Feature Enhancement Lung Tumor Segmentation Network (Guide-YNet) is proposed in this paper. Firstly, a double-encoder single-decoder U-Net is used as the backbone in this model, a single-coder single-decoder U-Net is used to generate the saliency guided feature using PET image and… More >