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  • Open Access

    ARTICLE

    Knee Osteoarthritis Classification Using X-Ray Images Based on Optimal Deep Neural Network

    Abdul Haseeb1, Muhammad Attique Khan1,*, Faheem Shehzad1, Majed Alhaisoni2, Junaid Ali Khan1, Taerang Kim3, Jae-Hyuk Cha3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2397-2415, 2023, DOI:10.32604/csse.2023.040529

    Abstract X-Ray knee imaging is widely used to detect knee osteoarthritis due to ease of availability and lesser cost. However, the manual categorization of knee joint disorders is time-consuming, requires an expert person, and is costly. This article proposes a new approach to classifying knee osteoarthritis using deep learning and a whale optimization algorithm. Two pre-trained deep learning models (Efficientnet-b0 and Densenet201) have been employed for the training and feature extraction. Deep transfer learning with fixed hyperparameter values has been employed to train both selected models on the knee X-Ray images. In the next step, fusion… More >

  • Open Access

    ARTICLE

    Anatomic Correlates of Mitral Systolic Anterior Motion in Transposition of the Great Arteries Following Atrial Switch Operation

    Norman Aiad1,2,3,7, Mark V. Sherrid1,7, Adam J. Small1, Youssef Elnabawi1,7, Jodi Feinberg1, Leon Axel1,4, Ralph Mosca5, T. K. Susheel Kumar5, Michael Argilla6, Dan G. Halpern1,7,*

    Congenital Heart Disease, Vol.18, No.3, pp. 267-277, 2023, DOI:10.32604/chd.2023.025853

    Abstract Introduction: We sought to investigate whether the development of sub-pulmonic systolic anterior motion (SAM) may be inherent to the anatomy of the the mitral valve (MV) or affected by external factors, such as a dilated right ventricle or chest abnormalities in d-looped transposition of the great arteries post atrial switch operation (d-TGA/AtS). Methods: Analysis was performed of clinical and cardiac imaging studies acquired on 19 adult patients with d-TGA/AtS (age 42 ± 6 years old, 56% male) between 2015–2019. Echocardiography data included mitral apparatus anatomy, and CT/MRI data included biventricular dimensions, function, and Haller index… More >

  • Open Access

    ARTICLE

    A Hybrid Attention-Based Residual Unet for Semantic Segmentation of Brain Tumor

    Wajiha Rahim Khan1, Tahir Mustafa Madni1, Uzair Iqbal Janjua1, Umer Javed2, Muhammad Attique Khan3, Majed Alhaisoni4, Usman Tariq5, Jae-Hyuk Cha6,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.039188

    Abstract Segmenting brain tumors in Magnetic Resonance Imaging (MRI) volumes is challenging due to their diffuse and irregular shapes. Recently, 2D and 3D deep neural networks have become famous for medical image segmentation because of the availability of labelled datasets. However, 3D networks can be computationally expensive and require significant training resources. This research proposes a 3D deep learning model for brain tumor segmentation that uses lightweight feature extraction modules to improve performance without compromising contextual information or accuracy. The proposed model, called Hybrid Attention-Based Residual Unet (HA-RUnet), is based on the Unet architecture and utilizes… More >

  • Open Access

    ARTICLE

    Real-Time Multi-Feature Approximation Model-Based Efficient Brain Tumor Classification Using Deep Learning Convolution Neural Network Model

    Amarendra Reddy Panyala1,2, M. Baskar3,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3883-3899, 2023, DOI:10.32604/csse.2023.037050

    Abstract The deep learning models are identified as having a significant impact on various problems. The same can be adapted to the problem of brain tumor classification. However, several deep learning models are presented earlier, but they need better classification accuracy. An efficient Multi-Feature Approximation Based Convolution Neural Network (CNN) model (MFA-CNN) is proposed to handle this issue. The method reads the input 3D Magnetic Resonance Imaging (MRI) images and applies Gabor filters at multiple levels. The noise-removed image has been equalized for its quality by using histogram equalization. Further, the features like white mass, grey… More >

  • Open Access

    ARTICLE

    IRMIRS: Inception-ResNet-Based Network for MRI Image Super-Resolution

    Wazir Muhammad1, Zuhaibuddin Bhutto2,*, Salman Masroor3,4, Murtaza Hussain Shaikh5, Jalal Shah2, Ayaz Hussain1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438

    Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because More >

  • Open Access

    ARTICLE

    Lung Cancer Segmentation with Three-Parameter Logistic Type Distribution Model

    Debnath Bhattacharyya1, Eali. Stephen Neal Joshua2, N. Thirupathi Rao2, Yung-cheol Byun3,*

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1447-1465, 2023, DOI:10.32604/cmc.2023.031878

    Abstract Lung cancer is the leading cause of mortality in the world affecting both men and women equally. When a radiologist just focuses on the patient’s body, it increases the amount of strain on the radiologist and the likelihood of missing pathological information such as abnormalities are increased. One of the primary objectives of this research work is to develop computer-assisted diagnosis and detection of lung cancer. It also intends to make it easier for radiologists to identify and diagnose lung cancer accurately. The proposed strategy which was based on a unique image feature, took into… More >

  • Open Access

    ARTICLE

    SNCDM: Spinal Tumor Detection from MRI Images Using Optimized Super-Pixel Segmentation

    T. Merlin Inbamalar1,*, Dhandapani Samiappan2, R. Ramesh3

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1899-1913, 2023, DOI:10.32604/iasc.2023.031202

    Abstract Conferring to the American Association of Neurological Surgeons (AANS) survey, 85% to 99% of people are affected by spinal cord tumors. The symptoms are varied depending on the tumor’s location and size. Up-to-the-minute, back pain is one of the essential symptoms, but it does not have a specific symptom to recognize at the earlier stage. Numerous significant research studies have been conducted to improve spine tumor recognition accuracy. Nevertheless, the traditional systems are consuming high time to extract the specific region and features. Improper identification of the tumor region affects the predictive tumor rate and More >

  • Open Access

    ARTICLE

    CE-EEN-B0: Contour Extraction Based Extended EfficientNet-B0 for Brain Tumor Classification Using MRI Images

    Abishek Mahesh1, Deeptimaan Banerjee1, Ahona Saha1, Manas Ranjan Prusty2,*, A. Balasundaram2

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5967-5982, 2023, DOI:10.32604/cmc.2023.033920

    Abstract A brain tumor is the uncharacteristic progression of tissues in the brain. These are very deadly, and if it is not diagnosed at an early stage, it might shorten the affected patient’s life span. Hence, their classification and detection play a critical role in treatment. Traditional Brain tumor detection is done by biopsy which is quite challenging. It is usually not preferred at an early stage of the disease. The detection involves Magnetic Resonance Imaging (MRI), which is essential for evaluating the tumor. This paper aims to identify and detect brain tumors based on their… More >

  • Open Access

    ARTICLE

    Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

    Muhammad Javaid Iqbal1, Muhammad Waseem Iqbal2, Muhammad Anwar3,*, Muhammad Murad Khan4, Abd Jabar Nazimi5, Mohammad Nazir Ahmad6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5267-5281, 2023, DOI:10.32604/cmc.2023.033024

    Abstract The brain tumour is the mass where some tissues become old or damaged, but they do not die or not leave their space. Mainly brain tumour masses occur due to malignant masses. These tissues must die so that new tissues are allowed to be born and take their place. Tumour segmentation is a complex and time-taking problem due to the tumour’s size, shape, and appearance variation. Manually finding such masses in the brain by analyzing Magnetic Resonance Images (MRI) is a crucial task for experts and radiologists. Radiologists could not work for large volume images… More >

  • Open Access

    ARTICLE

    De-Noising Brain MRI Images by Mixing Concatenation and Residual Learning (MCR)

    Kazim Ali1,*, Adnan N. Qureshi1, Muhammad Shahid Bhatti2, Abid Sohail2, Muhammad Hijji3, Atif Saeed2

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1167-1186, 2023, DOI:10.32604/csse.2023.032508

    Abstract Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is… More >

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