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  • Open Access

    ARTICLE

    Explainable Conformer Network for Detection of COVID-19 Pneumonia from Chest CT Scan: From Concepts toward Clinical Explainability

    Mohamed Abdel-Basset1, Hossam Hawash1, Mohamed Abouhawwash2,3,*, S. S. Askar4, Alshaimaa A. Tantawy1

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1171-1187, 2024, DOI:10.32604/cmc.2023.044425

    Abstract The early implementation of treatment therapies necessitates the swift and precise identification of COVID-19 pneumonia by the analysis of chest CT scans. This study aims to investigate the indispensable need for precise and interpretable diagnostic tools for improving clinical decision-making for COVID-19 diagnosis. This paper proposes a novel deep learning approach, called Conformer Network, for explainable discrimination of viral pneumonia depending on the lung Region of Infections (ROI) within a single modality radiographic CT scan. Firstly, an efficient U-shaped transformer network is integrated for lung image segmentation. Then, a robust transfer learning technique is introduced to design a robust feature… 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

    A Novel Unsupervised MRI Synthetic CT Image Generation Framework with Registration Network

    Liwei Deng1, Henan Sun1, Jing Wang2, Sijuan Huang3, Xin Yang3,*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2271-2287, 2023, DOI:10.32604/cmc.2023.039062

    Abstract In recent years, radiotherapy based only on Magnetic Resonance (MR) images has become a hot spot for radiotherapy planning research in the current medical field. However, functional computed tomography (CT) is still needed for dose calculation in the clinic. Recent deep-learning approaches to synthesized CT images from MR images have raised much research interest, making radiotherapy based only on MR images possible. In this paper, we proposed a novel unsupervised image synthesis framework with registration networks. This paper aims to enforce the constraints between the reconstructed image and the input image by registering the reconstructed image with the input image… More >

  • Open Access

    ARTICLE

    Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System

    Nojood O Aljehane*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3109-3126, 2023, DOI:10.32604/csse.2023.038042

    Abstract Medical image analysis is an active research topic, with thousands of studies published in the past few years. Transfer learning (TL) including convolutional neural networks (CNNs) focused to enhance efficiency on an innovative task using the knowledge of the same tasks learnt in advance. It has played a major role in medical image analysis since it solves the data scarcity issue along with that it saves hardware resources and time. This study develops an Enhanced Tunicate Swarm Optimization with Transfer Learning Enabled Medical Image Analysis System (ETSOTL-MIAS). The goal of the ETSOTL-MIAS technique lies in the identification and classification of… More >

  • Open Access

    ARTICLE

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

    Sixian Chan1,2, Binghui Wu1, Guodao Zhang3, Yuan Yao4, Hongqiang Wang2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 411-428, 2024, DOI:10.32604/cmes.2023.029485

    Abstract In clinical practice, the microscopic examination of urine sediment is considered an important in vitro examination with many broad applications. Measuring the amount of each type of urine sediment allows for screening, diagnosis and evaluation of kidney and urinary tract disease, providing insight into the specific type and severity. However, manual urine sediment examination is labor-intensive, time-consuming, and subjective. Traditional machine learning based object detection methods require hand-crafted features for localization and classification, which have poor generalization capabilities and are difficult to quickly and accurately detect the number of urine sediments. Deep learning based object detection methods have the potential… More > Graphic Abstract

    Learning Discriminatory Information for Object Detection on Urine Sediment Image

  • Open Access

    ARTICLE

    Robust Deep Learning Model for Black Fungus Detection Based on Gabor Filter and Transfer Learning

    Esraa Hassan1, Fatma M. Talaat1, Samah Adel2, Samir Abdelrazek3, Ahsan Aziz4, Yunyoung Nam4,*, Nora El-Rashidy1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1507-1525, 2023, DOI:10.32604/csse.2023.037493

    Abstract Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases. Recently, some COVID-19 survivors, especially those with co-morbid diseases, have been susceptible to black fungus. Therefore, recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms. This paper proposes a novel ensemble deep-learning model that includes three pre-trained models: reset (50), VGG (19), and Inception. Our approach is medically intuitive and efficient compared to the traditional deep learning models. An image dataset was aggregated from various resources and divided into two classes: a black fungus class… More >

  • Open Access

    ARTICLE

    TC-Fuse: A Transformers Fusing CNNs Network for Medical Image Segmentation

    Peng Geng1, Ji Lu1, Ying Zhang2,*, Simin Ma1, Zhanzhong Tang2, Jianhua Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 2001-2023, 2023, DOI:10.32604/cmes.2023.027127

    Abstract In medical image segmentation task, convolutional neural networks (CNNs) are difficult to capture long-range dependencies, but transformers can model the long-range dependencies effectively. However, transformers have a flexible structure and seldom assume the structural bias of input data, so it is difficult for transformers to learn positional encoding of the medical images when using fewer images for training. To solve these problems, a dual branch structure is proposed. In one branch, Mix-Feed-Forward Network (Mix-FFN) and axial attention are adopted to capture long-range dependencies and keep the translation invariance of the model. Mix-FFN whose depth-wise convolutions can provide position information is… More >

  • Open Access

    ARTICLE

    A Content-Based Medical Image Retrieval Method Using Relative Difference-Based Similarity Measure

    Ali Ahmed1,*, Alaa Omran Almagrabi2, Omar M. Barukab3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2355-2370, 2023, DOI:10.32604/iasc.2023.039847

    Abstract Content-based medical image retrieval (CBMIR) is a technique for retrieving medical images based on automatically derived image features. There are many applications of CBMIR, such as teaching, research, diagnosis and electronic patient records. Several methods are applied to enhance the retrieval performance of CBMIR systems. Developing new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these systems. This study proposes the relative difference-based similarity measure (RDBSM) for CBMIR. The new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional… More >

  • Open Access

    ARTICLE

    Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

    Bhawna Goyal1,*, Ayush Dogra2, Rahul Khoond1, Dawa Chyophel Lepcha1, Vishal Goyal3, Steven L. Fernandes4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 311-327, 2023, DOI:10.32604/cmc.2023.038398

    Abstract The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input… More >

  • Open Access

    ARTICLE

    Identifying Severity of COVID-19 Medical Images by Categorizing Using HSDC Model

    K. Ravishankar*, C. Jothikumar

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 613-635, 2023, DOI:10.32604/csse.2023.038343

    Abstract Since COVID-19 infections are increasing all over the world, there is a need for developing solutions for its early and accurate diagnosis is a must. Detection methods for COVID-19 include screening methods like Chest X-rays and Computed Tomography (CT) scans. More work must be done on preprocessing the datasets, such as eliminating the diaphragm portions, enhancing the image intensity, and minimizing noise. In addition to the detection of COVID-19, the severity of the infection needs to be estimated. The HSDC model is proposed to solve these problems, which will detect and classify the severity of COVID-19 from X-ray and CT-scan… More >

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