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  • Open Access

    ARTICLE

    A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

    Jawad Rasheed1,*, Shtwai Alsubai2

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4239-4259, 2023, DOI:10.32604/cmc.2023.031969 - 31 October 2022

    Abstract Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical… More >

  • Open Access

    ARTICLE

    Hybrid Optimized Learning for Lung Cancer Classification

    R. Vidhya1,*, T. T. Mirnalinee2

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 911-925, 2022, DOI:10.32604/iasc.2022.025060 - 03 May 2022

    Abstract Computer tomography (CT) scan images can provide more helpful diagnosis information regarding the lung cancers. Many machine learning and deep learning algorithms are formulated using CT input scan images for the improvisation in diagnosis and treatment process. But, designing an accurate and intelligent system still remains in darker side of the research side. This paper proposes the novel classification model which works on the principle of fused features and optimized learning network. The proposed framework incorporates the principle of saliency maps as a first tier segmentation, which is then fused with deep convolutional neural networks… More >

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