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  • Open Access


    Automatic Finding of Brain-Tumour Group Using CNN Segmentation and Moth-Flame-Algorithm, Selected Deep and Handcrafted Features

    Imad Saud Al Naimi1,2,*, Syed Alwee Aljunid Syed Junid1, Muhammad lmran Ahmad1,*, K. Suresh Manic2,3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2585-2608, 2024, DOI:10.32604/cmc.2024.046461

    Abstract Augmentation of abnormal cells in the brain causes brain tumor (BT), and early screening and treatment will reduce its harshness in patients. BT’s clinical level screening is usually performed with Magnetic Resonance Imaging (MRI) due to its multi-modality nature. The overall aims of the study is to introduce, test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans, facilitating improved accuracy. The research intends to devise a reliable framework for detecting the BT region in the two-dimensional (2D) MRI slice, and identifying its class with improved… More >

  • Open Access


    A Novel Handcrafted with Deep Features Based Brain Tumor Diagnosis Model

    Abdul Rahaman Wahab Sait1,*, Mohamad Khairi Ishak2

    Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2057-2070, 2023, DOI:10.32604/iasc.2023.029602

    Abstract In healthcare sector, image classification is one of the crucial problems that impact the quality output from image processing domain. The purpose of image classification is to categorize different healthcare images under various class labels which in turn helps in the detection and management of diseases. Magnetic Resonance Imaging (MRI) is one of the effective non-invasive strategies that generate a huge and distinct number of tissue contrasts in every imaging modality. This technique is commonly utilized by healthcare professionals for Brain Tumor (BT) diagnosis. With recent advancements in Machine Learning (ML) and Deep Learning (DL)… More >

  • Open Access


    Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

    Vasumathi Devi Majety1, N. Sharmili2, Chinmaya Ranjan Pattanaik3, E. Laxmi Lydia4, Subhi R. M. Zeebaree5, Sarmad Nozad Mahmood6, Ali S. Abosinnee7, Ahmed Alkhayyat8,*

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 4393-4406, 2022, DOI:10.32604/cmc.2022.031109

    Abstract Histopathology is the investigation of tissues to identify the symptom of abnormality. The histopathological procedure comprises gathering samples of cells/tissues, setting them on the microscopic slides, and staining them. The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge. At the same time, deep learning (DL) techniques are able to derive features, extract data, and learn advanced abstract data representation. With this view, this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification (EHCDL-HIC) model. The proposed EHCDL-HIC technique initially performs Weiner filtering based… More >

  • Open Access


    Optimal Fusion-Based Handcrafted with Deep Features for Brain Cancer Classification

    Mahmoud Ragab1,2,3,*, Sultanah M. Alshammari4, Amer H. Asseri2,5, Waleed K. Almutiry6

    CMC-Computers, Materials & Continua, Vol.73, No.1, pp. 801-815, 2022, DOI:10.32604/cmc.2022.029140

    Abstract Brain cancer detection and classification is done utilizing distinct medical imaging modalities like computed tomography (CT), or magnetic resonance imaging (MRI). An automated brain cancer classification using computer aided diagnosis (CAD) models can be designed to assist radiologists. With the recent advancement in computer vision (CV) and deep learning (DL) models, it is possible to automatically detect the tumor from images using a computer-aided design. This study focuses on the design of automated Henry Gas Solubility Optimization with Fusion of Handcrafted and Deep Features (HGSO-FHDF) technique for brain cancer classification. The proposed HGSO-FHDF technique aims… More >

  • Open Access


    Gastrointestinal Tract Infections Classification Using Deep Learning

    Muhammad Ramzan1, Mudassar Raza1, Muhammad Sharif1, Muhammad Attique Khan2, Yunyoung Nam3,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3239-3257, 2021, DOI:10.32604/cmc.2021.015920

    Abstract Automatic gastrointestinal (GI) tract disease recognition is an important application of biomedical image processing. Conventionally, microscopic analysis of pathological tissue is used to detect abnormal areas of the GI tract. The procedure is subjective and results in significant inter-/intra-observer variations in disease detection. Moreover, a huge frame rate in video endoscopy is an overhead for the pathological findings of gastroenterologists to observe every frame with a detailed examination. Consequently, there is a huge demand for a reliable computer-aided diagnostic system (CADx) for diagnosing GI tract diseases. In this work, a CADx was proposed for the… More >

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