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  • Open Access

    ARTICLE

    Brain Tumor Detection and Classification Using PSO and Convolutional Neural Network

    Muhammad Ali1, Jamal Hussain Shah1, Muhammad Attique Khan2, Majed Alhaisoni3, Usman Tariq4, Tallha Akram5, Ye Jin Kim6, Byoungchol Chang7,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4501-4518, 2022, DOI:10.32604/cmc.2022.030392 - 28 July 2022

    Abstract Tumor detection has been an active research topic in recent years due to the high mortality rate. Computer vision (CV) and image processing techniques have recently become popular for detecting tumors in MRI images. The automated detection process is simpler and takes less time than manual processing. In addition, the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians. We proposed a new framework for tumor detection as well as tumor classification into relevant categories in this paper. For tumor segmentation, the proposed framework employs the Particle Swarm… More >

  • Open Access

    ARTICLE

    A Two-Tier Framework Based on GoogLeNet and YOLOv3 Models for Tumor Detection in MRI

    Farman Ali1, Sadia Khan2, Arbab Waseem Abbas2, Babar Shah3, Tariq Hussain2, Dongho Song4,*, Shaker EI-Sappagh5,6, Jaiteg Singh7

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 73-92, 2022, DOI:10.32604/cmc.2022.024103 - 24 February 2022

    Abstract Medical Image Analysis (MIA) is one of the active research areas in computer vision, where brain tumor detection is the most investigated domain among researchers due to its deadly nature. Brain tumor detection in magnetic resonance imaging (MRI) assists radiologists for better analysis about the exact size and location of the tumor. However, the existing systems may not efficiently classify the human brain tumors with significantly higher accuracies. In addition, smart and easily implementable approaches are unavailable in 2D and 3D medical images, which is the main problem in detecting the tumor. In this paper, More >

  • Open Access

    ARTICLE

    Brain Tumor Detection and Segmentation Using RCNN

    Maham Khan1, Syed Adnan Shah1, Tenvir Ali2, Quratulain2, Aymen Khan2, Gyu Sang Choi3,*

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 5005-5020, 2022, DOI:10.32604/cmc.2022.023007 - 14 January 2022

    Abstract Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors’. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification… More >

  • Open Access

    ARTICLE

    Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

    Oday Ali Hassen1, Sarmad Omar Abter2, Ansam A. Abdulhussein3, Saad M. Darwish4,*, Yasmine M. Ibrahim4, Walaa Sheta5

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 961-981, 2021, DOI:10.32604/cmc.2021.014404 - 22 March 2021

    Abstract Medical image segmentation has consistently been a significant topic of research and a prominent goal, particularly in computer vision. Brain tumor research plays a major role in medical imaging applications by providing a tremendous amount of anatomical and functional knowledge that enhances and allows easy diagnosis and disease therapy preparation. To prevent or minimize manual segmentation error, automated tumor segmentation, and detection became the most demanding process for radiologists and physicians as the tumor often has complex structures. Many methods for detection and segmentation presently exist, but all lack high accuracy. This paper’s key contribution… More >

  • Open Access

    ARTICLE

    Liver-Tumor Detection Using CNN ResUNet

    Muhammad Sohaib Aslam1, Muhammad Younas1, Muhammad Umar Sarwar1, Muhammad Arif Shah2,*, Atif Khan3, M. Irfan Uddin4, Shafiq Ahmad5, Muhammad Firdausi5, Mazen Zaindin6

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1899-1914, 2021, DOI:10.32604/cmc.2021.015151 - 05 February 2021

    Abstract Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of… More >

  • Open Access

    ARTICLE

    A Learning Based Brain Tumor Detection System

    Sultan Noman Qasem1,2, Amar Nazar3, Attia Qamar4, Shahaboddin Shamshirband5,6,*, Ahmad Karim4

    CMC-Computers, Materials & Continua, Vol.59, No.3, pp. 713-727, 2019, DOI:10.32604/cmc.2019.05617

    Abstract Brain tumor is one of the most dangerous disease that causes due to uncontrollable and abnormal cell partition. In this paper, we have used MRI brain scan in comparison with CT brain scan as it is less harmful to detect brain tumor. We considered watershed segmentation technique for brain tumor detection. The proposed methodology is divided as follows: pre-processing, computing foreground applying watershed, extract and supply features to machine learning algorithms. Consequently, this study is tested on big data set of images and we achieved acceptable accuracy from K-NN classification algorithm in detection of brain More >

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