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  • Open Access

    ARTICLE

    Classification of Brain Tumors Using Hybrid Feature Extraction Based on Modified Deep Learning Techniques

    Tawfeeq Shawly1, Ahmed Alsheikhy2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 425-443, 2023, DOI:10.32604/cmc.2023.040561

    Abstract According to the World Health Organization (WHO), Brain Tumors (BrT) have a high rate of mortality across the world. The mortality rate, however, decreases with early diagnosis. Brain images, Computed Tomography (CT) scans, Magnetic Resonance Imaging scans (MRIs), segmentation, analysis, and evaluation make up the critical tools and steps used to diagnose brain cancer in its early stages. For physicians, diagnosis can be challenging and time-consuming, especially for those with little expertise. As technology advances, Artificial Intelligence (AI) has been used in various domains as a diagnostic tool and offers promising outcomes. Deep-learning techniques are especially useful and have achieved… More >

  • Open Access

    ARTICLE

    IoMT Enabled Melanoma Detection Using Improved Region Growing Lesion Boundary Extraction

    Tanzila Saba1, Rabia Javed2,3, Mohd Shafry Mohd Rahim2, Amjad Rehman1,*, Saeed Ali Bahaj4

    CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 6219-6237, 2022, DOI:10.32604/cmc.2022.020865

    Abstract The Internet of Medical Things (IoMT) and cloud-based healthcare applications, services are beneficial for better decision-making in recent years. Melanoma is a deadly cancer with a higher mortality rate than other skin cancer types such as basal cell, squamous cell, and Merkel cell. However, detection and treatment at an early stage can result in a higher chance of survival. The classical methods of detection are expensive and labor-intensive. Also, they rely on a trained practitioner's level, and the availability of the needed equipment is essential for the early detection of Melanoma. The current improvement in computer-aided systems is providing very… More >

  • Open Access

    ARTICLE

    COVID-19 Diagnosis Using Transfer-Learning Techniques

    Mohammed Faisal1,*, Fahad Albogamy2, Hebah ElGibreen3, Mohammed Algabri3, Syed Ahad M. Alvi1, Mansour Alsulaiman3

    Intelligent Automation & Soft Computing, Vol.29, No.3, pp. 649-667, 2021, DOI:10.32604/iasc.2021.017898

    Abstract COVID-19 was first discovered in Wuhan, China, in December 2019 and has since spread worldwide. An automated and fast diagnosis system needs to be developed for early and effective COVID-19 diagnosis. Hence, we propose two- and three-classifier diagnosis systems for classifying COVID-19 cases using transfer-learning techniques. These systems can classify X-ray images into three categories: healthy, COVID-19, and pneumonia cases. We used two X-ray image datasets (DATASET-1 and DATASET-2) collected from state-of-the-art studies and train the systems using deep learning architectures, such as VGG-19, NASNet, and MobileNet2, on these datasets. According to the validation and testing results, our proposed diagnosis… More >

  • Open Access

    ARTICLE

    A Convolutional Neural Network Classifier VGG-19 Architecture for Lesion Detection and Grading in Diabetic Retinopathy Based on Deep Learning

    V. Sudha1,*, T. R. Ganeshbabu2

    CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 827-842, 2021, DOI:10.32604/cmc.2020.012008

    Abstract Diabetic Retinopathy (DR) is a type of disease in eyes as a result of a diabetic condition that ends up damaging the retina, leading to blindness or loss of vision. Morphological and physiological retinal variations involving slowdown of blood flow in the retina, elevation of leukocyte cohesion, basement membrane dystrophy, and decline of pericyte cells, develop. As DR in its initial stage has no symptoms, early detection and automated diagnosis can prevent further visual damage. In this research, using a Deep Neural Network (DNN), segmentation methods are proposed to detect the retinal defects such as exudates, hemorrhages, microaneurysms from digital… More >

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