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  • Open Access


    Enhancing Multi-Modality Medical Imaging: A Novel Approach with Laplacian Filter + Discrete Fourier Transform Pre-Processing and Stationary Wavelet Transform Fusion

    Mian Muhammad Danyal1,2, Sarwar Shah Khan3,4,*, Rahim Shah Khan5, Saifullah Jan2, Naeem ur Rahman6

    Journal of Intelligent Medicine and Healthcare, Vol.2, pp. 35-53, 2024, DOI:10.32604/jimh.2024.051340

    Abstract Multi-modality medical images are essential in healthcare as they provide valuable insights for disease diagnosis and treatment. To harness the complementary data provided by various modalities, these images are amalgamated to create a single, more informative image. This fusion process enhances the overall quality and comprehensiveness of the medical imagery, aiding healthcare professionals in making accurate diagnoses and informed treatment decisions. In this study, we propose a new hybrid pre-processing approach, Laplacian Filter + Discrete Fourier Transform (LF+DFT), to enhance medical images before fusion. The LF+DFT approach highlights key details, captures small information, and sharpens… More >

  • Open Access


    Fine-Grained Ship Recognition Based on Visible and Near-Infrared Multimodal Remote Sensing Images: Dataset, Methodology and Evaluation

    Shiwen Song, Rui Zhang, Min Hu*, Feiyao Huang

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5243-5271, 2024, DOI:10.32604/cmc.2024.050879

    Abstract Fine-grained recognition of ships based on remote sensing images is crucial to safeguarding maritime rights and interests and maintaining national security. Currently, with the emergence of massive high-resolution multi-modality images, the use of multi-modality images for fine-grained recognition has become a promising technology. Fine-grained recognition of multi-modality images imposes higher requirements on the dataset samples. The key to the problem is how to extract and fuse the complementary features of multi-modality images to obtain more discriminative fusion features. The attention mechanism helps the model to pinpoint the key information in the image, resulting in a… More >

  • Open Access


    A Dual Discriminator Method for Generalized Zero-Shot Learning

    Tianshu Wei1, Jinjie Huang1,2,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1599-1612, 2024, DOI:10.32604/cmc.2024.048098

    Abstract Zero-shot learning enables the recognition of new class samples by migrating models learned from semantic features and existing sample features to things that have never been seen before. The problems of consistency of different types of features and domain shift problems are two of the critical issues in zero-shot learning. To address both of these issues, this paper proposes a new modeling structure. The traditional approach mapped semantic features and visual features into the same feature space; based on this, a dual discriminator approach is used in the proposed model. This dual discriminator approach can… More >

  • Open Access


    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First,… More >

  • Open Access


    Augmented Deep Multi-Granularity Pose-Aware Feature Fusion Network for Visible-Infrared Person Re-Identification

    Zheng Shi, Wanru Song*, Junhao Shan, Feng Liu

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3467-3488, 2023, DOI:10.32604/cmc.2023.045849

    Abstract Visible-infrared Cross-modality Person Re-identification (VI-ReID) is a critical technology in smart public facilities such as cities, campuses and libraries. It aims to match pedestrians in visible light and infrared images for video surveillance, which poses a challenge in exploring cross-modal shared information accurately and efficiently. Therefore, multi-granularity feature learning methods have been applied in VI-ReID to extract potential multi-granularity semantic information related to pedestrian body structure attributes. However, existing research mainly uses traditional dual-stream fusion networks and overlooks the core of cross-modal learning networks, the fusion module. This paper introduces a novel network called the… More >

  • Open Access


    Review of Visible-Infrared Cross-Modality Person Re-Identification

    Yinyin Zhang*

    Journal of New Media, Vol.5, No.1, pp. 23-31, 2023, DOI:10.32604/jnm.2023.038580

    Abstract Person re-identification (ReID) is a sub-problem under image retrieval. It is a technology that uses computer vision to identify a specific pedestrian in a collection of pictures or videos. The pedestrian image under cross-device is taken from a monitored pedestrian image. At present, most ReID methods deal with the matching between visible and visible images, but with the continuous improvement of security monitoring system, more and more infrared cameras are used to monitor at night or in dim light. Due to the image differences between infrared camera and RGB camera, there is a huge visual More >

  • Open Access


    Mining Fine-Grain Face Forgery Cues with Fusion Modality

    Shufan Peng, Manchun Cai*, Tianliang Lu, Xiaowen Liu

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4025-4045, 2023, DOI:10.32604/cmc.2023.036688

    Abstract Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns. Despite the considerable progress in existing methods, we note that: Previous works overlooked fine-grain forgery cues with high transferability. Such cues positively impact the model’s accuracy and generalizability. Moreover, single-modality often causes overfitting of the model, and Red-Green-Blue (RGB) modal-only is not conducive to extracting the more detailed forgery traces. We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues. First, we propose two functional modules to reveal and locate the deeper… More >

  • Open Access


    Multi-Modality and Feature Fusion-Based COVID-19 Detection Through Long Short-Term Memory

    Noureen Fatima1, Rashid Jahangir2, Ghulam Mujtaba1, Adnan Akhunzada3,*, Zahid Hussain Shaikh4, Faiza Qureshi1

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4357-4374, 2022, DOI:10.32604/cmc.2022.023830

    Abstract The Coronavirus Disease 2019 (COVID-19) pandemic poses the worldwide challenges surpassing the boundaries of country, religion, race, and economy. The current benchmark method for the detection of COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) testing. Nevertheless, this testing method is accurate enough for the diagnosis of COVID-19. However, it is time-consuming, expensive, expert-dependent, and violates social distancing. In this paper, this research proposed an effective multi-modality-based and feature fusion-based (MMFF) COVID-19 detection technique through deep neural networks. In multi-modality, we have utilized the cough samples, breathe samples and sound samples of healthy as… More >

  • Open Access


    Melanoma Identification Through X-ray Modality Using Inception-v3 Based Convolutional Neural Network

    Saad Awadh Alanazi*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 37-55, 2022, DOI:10.32604/cmc.2022.020118

    Abstract Melanoma, also called malignant melanoma, is a form of skin cancer triggered by an abnormal proliferation of the pigment-producing cells, which give the skin its color. Melanoma is one of the skin diseases, which is exceptionally and globally dangerous, Skin lesions are considered to be a serious disease. Dermoscopy-based early recognition and detection procedure is fundamental for melanoma treatment. Early detection of melanoma using dermoscopy images improves survival rates significantly. At the same time, well-experienced dermatologists dominate the precision of diagnosis. However, precise melanoma recognition is incredibly hard due to several factors: low contrast between… More >

  • Open Access


    Lactoferrin-Conjugated Polylactic Acid Nanobubbles Encapsulated Perfluoropentane as a Contrast Agent for Ultrasound/Magnetic Resonance Dual-Modality Imaging

    Liqiong Ding1, Pingsheng Li2, Liu He1, Fengnan Xu1, Jieqiong Ding3,*, Binhua Luo1,*

    Journal of Renewable Materials, Vol.10, No.3, pp. 767-780, 2022, DOI:10.32604/jrm.2022.016903

    Abstract The development of contrast agents that can be activated by multiple modes is of great significance for tumor diagnosis. In this study, the lactoferrin (Lf)-conjugated polylactic acid (PLLA) nanobubbles (Lf-PLLA NBs) were used to encapsulate liquid perfluoropentane (PFP) with the double emulsion method, creating PFP loaded (PFP/Lf-PLLA) NBs for the ultrasound/magnetic resonance dual-modality imaging of subcutaneous tumor. The particle diameter and stability of nanobubbles were investigated by photon correlation spectroscopy. The biocompatibility of nanobubbles was preliminarily evaluated by cell proliferation and migration assay, hemolysis rate, and blood biochemistry analysis. A B-mode clinical ultrasound real-time imaging… More >

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