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  • Open Access

    ARTICLE

    Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification

    Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, Shui-Hua Wang4, Kashif Javed5

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154 - 22 March 2021

    Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two More >

  • Open Access

    ARTICLE

    An Intelligent Incremental Filtering Feature Selection and Clustering Algorithm for Effective Classification

    U. Kanimozhi, D. Manjula

    Intelligent Automation & Soft Computing, Vol.24, No.4, pp. 701-709, 2018, DOI:10.1080/10798587.2017.1307626

    Abstract We are witnessing the era of big data computing where computing the resources is becoming the main bottleneck to deal with those large datasets. In the case of high-dimensional data where each view of data is of high dimensionality, feature selection is necessary for further improving the clustering and classification results. In this paper, we propose a new feature selection method, Incremental Filtering Feature Selection (IF2S) algorithm, and a new clustering algorithm, Temporal Interval based Fuzzy Minimal Clustering (TIFMC) algorithm that employs the Fuzzy Rough Set for selecting optimal subset of features and for effective More >

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