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  • Open Access

    ARTICLE

    Transformer-Based Cloud Detection Method for High-Resolution Remote Sensing Imagery

    Haotang Tan1, Song Sun2,*, Tian Cheng3, Xiyuan Shu2

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 661-678, 2024, DOI:10.32604/cmc.2024.052208 - 18 July 2024

    Abstract Cloud detection from satellite and drone imagery is crucial for applications such as weather forecasting and environmental monitoring. Addressing the limitations of conventional convolutional neural networks, we propose an innovative transformer-based method. This method leverages transformers, which are adept at processing data sequences, to enhance cloud detection accuracy. Additionally, we introduce a Cyclic Refinement Architecture that improves the resolution and quality of feature extraction, thereby aiding in the retention of critical details often lost during cloud detection. Our extensive experimental validation shows that our approach significantly outperforms established models, excelling in high-resolution feature extraction and More >

  • Open Access

    ARTICLE

    Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

    Bing Li1,2,*, Liangyu Wang1, Xia Liu1,2, Hongbin Fan1, Bo Wang3, Shoudi Tong1

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1543-1561, 2024, DOI:10.32604/cmc.2024.052009 - 18 July 2024

    Abstract Nuclear magnetic resonance imaging of breasts often presents complex backgrounds. Breast tumors exhibit varying sizes, uneven intensity, and indistinct boundaries. These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation. Thus, we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms. Initially, the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs. Subsequently, we devise a fusion network incorporating multi-scale features and boundary attention mechanisms for breast tumor segmentation. We incorporate multi-scale parallel dilated convolution modules into… More >

  • Open Access

    ARTICLE

    UNet Based on Multi-Object Segmentation and Convolution Neural Network for Object Recognition

    Nouf Abdullah Almujally1, Bisma Riaz Chughtai2, Naif Al Mudawi3, Abdulwahab Alazeb3, Asaad Algarni4, Hamdan A. Alzahrani5, Jeongmin Park6,*

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1563-1580, 2024, DOI:10.32604/cmc.2024.049333 - 18 July 2024

    Abstract The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes. Various technologies, such as augmented reality-driven scene integration, robotic navigation, autonomous driving, and guided tour systems, heavily rely on this type of scene comprehension. This paper presents a novel segmentation approach based on the UNet network model, aimed at recognizing multiple objects within an image. The methodology begins with the acquisition and preprocessing of the image, followed by segmentation using the fine-tuned UNet architecture. Afterward, we use an annotation tool to accurately label… More >

  • Open Access

    ARTICLE

    Refined Anam-Net: Lightweight Deep Learning Model for Improved Segmentation Performance of Optic Cup and Disc for Glaucoma Diagnosis

    Khursheed Aurangzeb*, Syed Irtaza Haider, Musaed Alhussein

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1381-1405, 2024, DOI:10.32604/cmc.2024.048987 - 18 July 2024

    Abstract In this work, we aim to introduce some modifications to the Anam-Net deep neural network (DNN) model for segmenting optic cup (OC) and optic disc (OD) in retinal fundus images to estimate the cup-to-disc ratio (CDR). The CDR is a reliable measure for the early diagnosis of Glaucoma. In this study, we developed a lightweight DNN model for OC and OD segmentation in retinal fundus images. Our DNN model is based on modifications to Anam-Net, incorporating an anamorphic depth embedding block. To reduce computational complexity, we employ a fixed filter size for all convolution layers… More >

  • Open Access

    RETRACTION

    Retraction: Deep Belief Network for Lung Nodule Segmentation and Cancer Detection

    Computer Systems Science and Engineering Editorial Office

    Computer Systems Science and Engineering, Vol.48, No.4, pp. 1083-1083, 2024, DOI:10.32604/csse.2024.054265 - 17 July 2024

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Enhancing Exam Preparation through Topic Modelling and Key Topic Identification

    Rudraneel Dutta*, Shreya Mohanty

    Journal on Artificial Intelligence, Vol.6, pp. 177-192, 2024, DOI:10.32604/jai.2024.050706 - 19 July 2024

    Abstract Traditionally, exam preparation involves manually analyzing past question papers to identify and prioritize key topics. This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation, Optical Character Recognition (OCR), and Latent Dirichlet Allocation (LDA) for topic modelling. This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers, aiding students in efficient exam preparation. The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify… More >

  • Open Access

    ARTICLE

    FDSC-YOLOv8: Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

    Rui Wang1, Zhihui Liu2,*, Hongdi Liu3, Baozhong Su4, Chuanyi Ma5

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 3035-3049, 2024, DOI:10.32604/cmes.2024.050806 - 08 July 2024

    Abstract In underground engineering, the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel structures. However, the dim and dusty environment inherent to underground engineering poses considerable challenges to crack segmentation. This paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8 (FDSC-YOLOv8) specifically designed for underground engineering structural surfaces. Firstly, to improve the extraction of multi-layer convolutional features, the fixed convolutional module is replaced with a deformable convolutional module. Secondly, the model’s receptive field is enhanced by introducing a multi-branch More >

  • Open Access

    ARTICLE

    Instance Segmentation of Characters Recognized in Palmyrene Aramaic Inscriptions

    Adéla Hamplová1,*, Alexey Lyavdansky2,*, Tomáš Novák1, Ondřej Svojše1, David Franc1, Arnošt Veselý1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2869-2889, 2024, DOI:10.32604/cmes.2024.050791 - 08 July 2024

    Abstract This study presents a single-class and multi-class instance segmentation approach applied to ancient Palmyrene inscriptions, employing two state-of-the-art deep learning algorithms, namely YOLOv8 and Roboflow 3.0. The goal is to contribute to the preservation and understanding of historical texts, showcasing the potential of modern deep learning methods in archaeological research. Our research culminates in several key findings and scientific contributions. We comprehensively compare the performance of YOLOv8 and Roboflow 3.0 in the context of Palmyrene character segmentation—this comparative analysis mainly focuses on the strengths and weaknesses of each algorithm in this context. We also created… More >

  • Open Access

    REVIEW

    A Comprehensive Systematic Review: Advancements in Skin Cancer Classification and Segmentation Using the ISIC Dataset

    Madiha Hameed1,3, Aneela Zameer1,*, Muhammad Asif Zahoor Raja2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2131-2164, 2024, DOI:10.32604/cmes.2024.050124 - 08 July 2024

    Abstract The International Skin Imaging Collaboration (ISIC) datasets are pivotal resources for researchers in machine learning for medical image analysis, especially in skin cancer detection. These datasets contain tens of thousands of dermoscopic photographs, each accompanied by gold-standard lesion diagnosis metadata. Annual challenges associated with ISIC datasets have spurred significant advancements, with research papers reporting metrics surpassing those of human experts. Skin cancers are categorized into melanoma and non-melanoma types, with melanoma posing a greater threat due to its rapid potential for metastasis if left untreated. This paper aims to address challenges in skin cancer detection… More >

  • Open Access

    ARTICLE

    A U-Shaped Network-Based Grid Tagging Model for Chinese Named Entity Recognition

    Yan Xiang1,2, Xuedong Zhao1,2, Junjun Guo1,2,*, Zhiliang Shi3, Enbang Chen3, Xiaobo Zhang3

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4149-4167, 2024, DOI:10.32604/cmc.2024.050229 - 20 June 2024

    Abstract Chinese named entity recognition (CNER) has received widespread attention as an important task of Chinese information extraction. Most previous research has focused on individually studying flat CNER, overlapped CNER, or discontinuous CNER. However, a unified CNER is often needed in real-world scenarios. Recent studies have shown that grid tagging-based methods based on character-pair relationship classification hold great potential for achieving unified NER. Nevertheless, how to enrich Chinese character-pair grid representations and capture deeper dependencies between character pairs to improve entity recognition performance remains an unresolved challenge. In this study, we enhance the character-pair grid representation… More >

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