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  • Open Access

    ARTICLE

    Multilevel Attention Unet Segmentation Algorithm for Lung Cancer Based on CT Images

    Huan Wang1, Shi Qiu1,2,*, Benyue Zhang1, Lixuan Xiao3

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1569-1589, 2024, DOI:10.32604/cmc.2023.046821

    Abstract Lung cancer is a malady of the lungs that gravely jeopardizes human health. Therefore, early detection and treatment are paramount for the preservation of human life. Lung computed tomography (CT) image sequences can explicitly delineate the pathological condition of the lungs. To meet the imperative for accurate diagnosis by physicians, expeditious segmentation of the region harboring lung cancer is of utmost significance. We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner, erect an interpretable model, and attain segmentation of lung cancer. The specific advancements can be encapsulated as follows:… More >

  • Open Access

    ARTICLE

    Enhanced Wolf Pack Algorithm (EWPA) and Dense-kUNet Segmentation for Arterial Calcifications in Mammograms

    Afnan M. Alhassan*

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2207-2223, 2024, DOI:10.32604/cmc.2024.046427

    Abstract Breast Arterial Calcification (BAC) is a mammographic decision dissimilar to cancer and commonly observed in elderly women. Thus identifying BAC could provide an expense, and be inaccurate. Recently Deep Learning (DL) methods have been introduced for automatic BAC detection and quantification with increased accuracy. Previously, classification with deep learning had reached higher efficiency, but designing the structure of DL proved to be an extremely challenging task due to overfitting models. It also is not able to capture the patterns and irregularities presented in the images. To solve the overfitting problem, an optimal feature set has been formed by Enhanced Wolf… More >

  • Open Access

    ARTICLE

    ThyroidNet: A Deep Learning Network for Localization and Classification of Thyroid Nodules

    Lu Chen1,#, Huaqiang Chen2,#, Zhikai Pan7, Sheng Xu2, Guangsheng Lai2, Shuwen Chen2,5,6, Shuihua Wang3,8, Xiaodong Gu2,6,*, Yudong Zhang3,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 361-382, 2024, DOI:10.32604/cmes.2023.031229

    Abstract Aim: This study aims to establish an artificial intelligence model, ThyroidNet, to diagnose thyroid nodules using deep learning techniques accurately. Methods: A novel method, ThyroidNet, is introduced and evaluated based on deep learning for the localization and classification of thyroid nodules. First, we propose the multitask TransUnet, which combines the TransUnet encoder and decoder with multitask learning. Second, we propose the DualLoss function, tailored to the thyroid nodule localization and classification tasks. It balances the learning of the localization and classification tasks to help improve the model’s generalization ability. Third, we introduce strategies for augmenting the data. Finally, we submit… More >

  • Open Access

    ARTICLE

    Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

    Fufang Li, Manlin Luo*, Ming Hu, Guobin Wang, Yan Chen

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2835-2850, 2023, DOI:10.32604/csse.2023.039765

    Abstract Liver cancer has the second highest incidence rate among all types of malignant tumors, and currently, its diagnosis heavily depends on doctors’ manual labeling of CT scan images, a process that is time-consuming and susceptible to subjective errors. To address the aforementioned issues, we propose an automatic segmentation model for liver and tumors called Res2Swin Unet, which is based on the Unet architecture. The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation, respectively. Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections, while Swin Transformer captures long-range dependencies and models the input… More >

  • Open Access

    ARTICLE

    A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

    Javed Rashid1,2, Imran Khan1, Irshad Ahmed Abbasi3, Muhammad Rizwan Saeed4, Mubbashar Saddique5,*, Mohamed Abbas6,7

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3897-3920, 2023, DOI:10.32604/cmc.2023.040356

    Abstract Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully… More >

  • Open Access

    ARTICLE

    A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

    Wajiha Rahim Khan1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Majed Alhaisoni4, Usman Tariq5, Jae-Hyuk Cha6,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188

    Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes residual blocks to extract low-… More >

  • Open Access

    ARTICLE

    HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

    S. Deva Kumar, S. Venkatramaphanikumar*, K. Venkata Krishna Kishore

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165

    Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge the model’s width by substituting… More >

  • Open Access

    ARTICLE

    Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

    Muwei Jian1,2,#,*, Ronghua Wu1,#, Hongyu Chen1, Lanqi Fu3, Chengdong Yang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 705-716, 2023, DOI:10.32604/cmes.2023.027425

    Abstract In intelligent perception and diagnosis of medical equipment, the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases (e.g., diabetes and hypertension). Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results. To address this challenge, we design a Dual-Branch-UNet framework, which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation. To be more explicit, we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net. Then, image… More >

  • Open Access

    ARTICLE

    Towards Robust Rain Removal with Unet++

    Boxia Hu1,2,*, Yaqi Sun3, Yufei Yang1,4, Ze Ouyang3, Feng Zhang3

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 879-890, 2023, DOI:10.32604/cmc.2023.035858

    Abstract Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data… More >

  • Open Access

    ARTICLE

    Semantic Segmentation by Using Down-Sampling and Subpixel Convolution: DSSC-UNet

    Young-Man Kwon, Sunghoon Bae, Dong-Keun Chung, Myung-Jae Lim*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 683-696, 2023, DOI:10.32604/cmc.2023.033370

    Abstract Recently, semantic segmentation has been widely applied to image processing, scene understanding, and many others. Especially, in deep learning-based semantic segmentation, the U-Net with convolutional encoder-decoder architecture is a representative model which is proposed for image segmentation in the biomedical field. It used max pooling operation for reducing the size of image and making noise robust. However, instead of reducing the complexity of the model, max pooling has the disadvantage of omitting some information about the image in reducing it. So, this paper used two diagonal elements of down-sampling operation instead of it. We think that the down-sampling feature maps… More >

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