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  • Open Access

    ARTICLE

    Segmentation and Classification of Stomach Abnormalities Using Deep Learning

    Javeria Naz1, Muhammad Attique Khan1, Majed Alhaisoni2, Oh-Young Song3,*, Usman Tariq4, Seifedine Kadry5

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 607-625, 2021, DOI:10.32604/cmc.2021.017101

    Abstract An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction… More >

  • Open Access

    REVIEW

    Importance of Features Selection, Attributes Selection, Challenges and Future Directions for Medical Imaging Data: A Review

    Nazish Naheed1, Muhammad Shaheen1, Sajid Ali Khan1, Mohammed Alawairdhi2,*, Muhammad Attique Khan3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 315-344, 2020, DOI:10.32604/cmes.2020.011380

    Abstract In the area of pattern recognition and machine learning, features play a key role in prediction. The famous applications of features are medical imaging, image classification, and name a few more. With the exponential growth of information investments in medical data repositories and health service provision, medical institutions are collecting large volumes of data. These data repositories contain details information essential to support medical diagnostic decisions and also improve patient care quality. On the other hand, this growth also made it difficult to comprehend and utilize data for various purposes. The results of imaging data can become biased because of… More >

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