Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (8)
  • Open Access


    Differentiate Xp11.2 Translocation Renal Cell Carcinoma from Computed Tomography Images and Clinical Data with ResNet-18 CNN and XGBoost

    Yanwen Lu1,#, Wenliang Ma1,#, Xiang Dong1,#, Mackenzie Brown2, Tong Lu3,*, Weidong Gan1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 347-362, 2023, DOI:10.32604/cmes.2023.024909

    Abstract This study aims to apply ResNet-18 convolutional neural network (CNN) and XGBoost to preoperative computed tomography (CT) images and clinical data for distinguishing Xp11.2 translocation renal cell carcinoma (Xp11.2 tRCC) from common subtypes of renal cell carcinoma (RCC) in order to provide patients with individualized treatment plans. Data from 45 patients with Xp11.2 tRCC from January 2007 to December 2021 are collected. Clear cell RCC (ccRCC), papillary RCC (pRCC), or chromophobe RCC (chRCC) can be detected from each patient. CT images are acquired in the following three phases: unenhanced, corticomedullary, and nephrographic. A unified framework is proposed for the classification… More >

  • Open Access


    A Lightweight CNN Based on Transfer Learning for COVID-19 Diagnosis

    Xiaorui Zhang1,2,3,*, Jie Zhou2, Wei Sun3,4, Sunil Kumar Jha5

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1123-1137, 2022, DOI:10.32604/cmc.2022.024589

    Abstract The key to preventing the COVID-19 is to diagnose patients quickly and accurately. Studies have shown that using Convolutional Neural Networks (CNN) to analyze chest Computed Tomography (CT) images is helpful for timely COVID-19 diagnosis. However, personal privacy issues, public chest CT data sets are relatively few, which has limited CNN's application to COVID-19 diagnosis. Also, many CNNs have complex structures and massive parameters. Even if equipped with the dedicated Graphics Processing Unit (GPU) for acceleration, it still takes a long time, which is not conductive to widespread application. To solve above problems, this paper proposes a lightweight CNN classification… More >

  • Open Access


    Modified UNet Model for Brain Stroke Lesion Segmentation on Computed Tomography Images

    Batyrkhan Omarov1,2,3, Azhar Tursynova1,*, Octavian Postolache4, Khaled Gamry5, Aidar Batyrbekov5, Sapargali Aldeshov6,7, Zhanar Azhibekova9, Marat Nurtas5,8, Akbayan Aliyeva6, Kadrzhan Shiyapov10,11

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4701-4717, 2022, DOI:10.32604/cmc.2022.020998

    Abstract The task of segmentation of brain regions affected by ischemic stroke is help to tackle important challenges of modern stroke imaging analysis. Unfortunately, at the moment, the models for solving this problem using machine learning methods are far from ideal. In this paper, we consider a modified 3D UNet architecture to improve the quality of stroke segmentation based on 3D computed tomography images. We use the ISLES 2018 (Ischemic Stroke Lesion Segmentation Challenge 2018) open dataset to train and test the proposed model. Interpretation of the obtained results, as well as the ideas for further experiments are included in the… More >

  • Open Access


    Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

    Javaria Amin1, Muhammad Sharif2, Muhammad Almas Anjum3, Yunyoung Nam4,*, Seifedine Kadry5, David Taniar6

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2451-2467, 2021, DOI:10.32604/cmc.2021.014199

    Abstract Coronavirus 19 (COVID-19) can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and… More >

  • Open Access


    Deep Learning Approach for COVID-19 Detection in Computed Tomography Images

    Mohamad Mahmoud Al Rahhal1, Yakoub Bazi2,*, Rami M. Jomaa3, Mansour Zuair2, Naif Al Ajlan2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2093-2110, 2021, DOI:10.32604/cmc.2021.014956

    Abstract With the rapid spread of the coronavirus disease 2019 (COVID-19) worldwide, the establishment of an accurate and fast process to diagnose the disease is important. The routine real-time reverse transcription-polymerase chain reaction (rRT-PCR) test that is currently used does not provide such high accuracy or speed in the screening process. Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques. In this study, a new convolutional neural network (CNN) framework for COVID-19 detection using computed tomography (CT) images is proposed. The EfficientNet architecture is applied as the backbone structure of the proposed network,… More >

  • Open Access


    Automatic Segmentation of Liver from Abdominal Computed Tomography Images Using Energy Feature

    Prabakaran Rajamanickam1, Shiloah Elizabeth Darmanayagam1,*, Sunil Retmin Raj Cyril Raj2

    CMC-Computers, Materials & Continua, Vol.67, No.1, pp. 709-722, 2021, DOI:10.32604/cmc.2021.014347

    Abstract Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography (CT) images. The segmentation of hepatic organ is more intricate task, owing to the fact that it possesses a sizeable quantum of vascularization. This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans. The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not. This involves segmentation of the region of interest (ROI) from… More >

  • Open Access


    Effect of Data Augmentation of Renal Lesion Image by Nine-layer Convolutional Neural Network in Kidney CT

    Liying Wang1 , Zhiqiang Xu2, Shuihua Wang3,4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.124, No.3, pp. 1001-1015, 2020, DOI:10.32604/cmes.2020.010753

    Abstract Artificial Intelligence (AI) becomes one hotspot in the field of the medical images analysis and provides rather promising solution. Although some research has been explored in smart diagnosis for the common diseases of urinary system, some problems remain unsolved completely A nine-layer Convolutional Neural Network (CNN) is proposed in this paper to classify the renal Computed Tomography (CT) images. Four group of comparative experiments prove the structure of this CNN is optimal and can achieve good performance with average accuracy about 92.07 ± 1.67%. Although our renal CT data is not very large, we do augment the training data by… More >

  • Open Access


    Novel Approach for Automatic Region of Interest and Seed Point Detection in CT Images Based on Temporal and Spatial Data

    Zhe Liu1, Charlie Maere1,*, Yuqing Song1

    CMC-Computers, Materials & Continua, Vol.59, No.2, pp. 669-686, 2019, DOI:10.32604/cmc.2019.04590

    Abstract Accurately finding the region of interest is a very vital step for segmenting organs in medical image processing. We propose a novel approach of automatically identifying region of interest in Computed Tomography Image (CT) images based on temporal and spatial data . Our method is a 3 stages approach, 1) We extract organ features from the CT images by adopting the Hounsfield filter. 2)We use these filtered features and introduce our novel approach of selecting observable feature candidates by calculating contours’ area and automatically detect a seed point. 3) We use a novel approach to track the growing region changes… More >

Displaying 1-10 on page 1 of 8. Per Page