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  • Open Access

    ARTICLE

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

    Awais Khan1, Chomyong Kim2, Jung-Yeon Kim2, Ahsan Aziz1, Yunyoung Nam3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1729-1755, 2024, DOI:10.32604/cmes.2024.049618

    Abstract Sleep posture surveillance is crucial for patient comfort, yet current systems face difficulties in providing comprehensive studies due to the obstruction caused by blankets. Precise posture assessment remains challenging because of the complex nature of the human body and variations in sleep patterns. Consequently, this study introduces an innovative method utilizing RGB and thermal cameras for comprehensive posture classification, thereby enhancing the analysis of body position and comfort. This method begins by capturing a dataset of sleep postures in the form of videos using RGB and thermal cameras, which depict six commonly adopted postures: supine,… More > Graphic Abstract

    Sleep Posture Classification Using RGB and Thermal Cameras Based on Deep Learning Model

  • Open Access

    ARTICLE

    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

    ARTICLE

    Optimized Deep Learning Approach for Efficient Diabetic Retinopathy Classification Combining VGG16-CNN

    Heba M. El-Hoseny1,*, Heba F. Elsepae2, Wael A. Mohamed2, Ayman S. Selmy2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1855-1872, 2023, DOI:10.32604/cmc.2023.042107

    Abstract Diabetic retinopathy is a critical eye condition that, if not treated, can lead to vision loss. Traditional methods of diagnosing and treating the disease are time-consuming and expensive. However, machine learning and deep transfer learning (DTL) techniques have shown promise in medical applications, including detecting, classifying, and segmenting diabetic retinopathy. These advanced techniques offer higher accuracy and performance. Computer-Aided Diagnosis (CAD) is crucial in speeding up classification and providing accurate disease diagnoses. Overall, these technological advancements hold great potential for improving the management of diabetic retinopathy. The study’s objective was to differentiate between different classes… More >

  • Open Access

    ARTICLE

    Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

    Deepika Saravagi1, Shweta Agrawal2,*, Manisha Saravagi3, Sanjiv K. Jain4, Bhisham Sharma5, Abolfazl Mehbodniya6,*, Subrata Chowdhury7, Julian L. Webber6

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2133-2151, 2023, DOI:10.32604/iasc.2023.039836

    Abstract Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 and CapsNet… More >

  • Open Access

    ARTICLE

    Identification of Tuberculosis and Coronavirus Patients Using Hybrid Deep Learning Models

    Mohammed A. Al Ghamdi*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 881-894, 2023, DOI:10.32604/cmc.2023.037826

    Abstract Considerable resources, technology, and efforts are being utilized worldwide to eradicate the coronavirus. Although certain measures taken to prevent the further spread of the disease have been successful, efforts to completely wipe out the coronavirus have been insufficient. Coronavirus patients have symptoms similar to those of chest Tuberculosis (TB) or pneumonia patients. Chest tuberculosis and coronavirus are similar because both diseases affect the lungs, cause coughing and produce an irregular respiratory system. Both diseases can be confirmed through X-ray imaging. It is a difficult task to diagnose COVID-19, as coronavirus testing kits are neither excessively… More >

  • Open Access

    ARTICLE

    HIUNET: A Hybrid Inception U-Net for Diagnosis of Diabetic Retinopathy

    S. Deva Kumar, S. Venkatramaphanikumar*, K. Venkata Krishna Kishore

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 1013-1032, 2023, DOI:10.32604/iasc.2023.038165

    Abstract Type 2 diabetes patients often suffer from microvascular complications of diabetes. These complications, in turn, often lead to vision impairment. Diabetic Retinopathy (DR) detection in its early stage can rescue people from long-term complications that could lead to permanent blindness. In this study, we propose a complex deep convolutional neural network architecture with an inception module for automated diagnosis of DR. The proposed novel Hybrid Inception U-Net (HIUNET) comprises various inception modules connected in the U-Net fashion using activation maximization and filter map to produce the image mask. First, inception blocks were used to enlarge… More >

  • Open Access

    ARTICLE

    Improved Siamese Palmprint Authentication Using Pre-Trained VGG16-Palmprint and Element-Wise Absolute Difference

    Mohamed Ezz, Waad Alanazi, Ayman Mohamed Mostafa*, Eslam Hamouda, Murtada K. Elbashir, Meshrif Alruily

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2299-2317, 2023, DOI:10.32604/csse.2023.036567

    Abstract Palmprint identification has been conducted over the last two decades in many biometric systems. High-dimensional data with many uncorrelated and duplicated features remains difficult due to several computational complexity issues. This paper presents an interactive authentication approach based on deep learning and feature selection that supports Palmprint authentication. The proposed model has two stages of learning; the first stage is to transfer pre-trained VGG-16 of ImageNet to specific features based on the extraction model. The second stage involves the VGG-16 Palmprint feature extraction in the Siamese network to learn Palmprint similarity. The proposed model achieves More >

  • Open Access

    ARTICLE

    Transfer Learning-Based Semi-Supervised Generative Adversarial Network for Malaria Classification

    Ibrar Amin1, Saima Hassan1, Samir Brahim Belhaouari2,*, Muhammad Hamza Azam3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6335-6349, 2023, DOI:10.32604/cmc.2023.033860

    Abstract Malaria is a lethal disease responsible for thousands of deaths worldwide every year. Manual methods of malaria diagnosis are time-consuming that require a great deal of human expertise and efforts. Computer-based automated diagnosis of diseases is progressively becoming popular. Although deep learning models show high performance in the medical field, it demands a large volume of data for training which is hard to acquire for medical problems. Similarly, labeling of medical images can be done with the help of medical experts only. Several recent studies have utilized deep learning models to develop efficient malaria diagnostic More >

  • Open Access

    ARTICLE

    Block-Wise Neural Network for Brain Tumor Identification in Magnetic Resonance Images

    Abdullah A. Asiri1, Muhammad Aamir2, Ahmad Shaf2,*, Tariq Ali2, Muhammad Zeeshan3, Muhammad Irfan4, Khalaf A. Alshamrani1, Hassan A. Alshamrani1, Fawaz F. Alqahtani1, Ali H. D. Alshehri1

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 5735-5753, 2022, DOI:10.32604/cmc.2022.031747

    Abstract The precise brain tumor diagnosis is critical and shows a vital role in the medical support for treating tumor patients. Manual brain tumor segmentation for cancer analysis from many Magnetic Resonance Images (MRIs) created in medical practice is a problematic and timewasting task for experts. As a result, there is a critical necessity for more accurate computer-aided methods for early tumor detection. To remove this gap, we enhanced the computational power of a computer-aided system by proposing a fine-tuned Block-Wise Visual Geometry Group19 (BW-VGG19) architecture. In this method, a pre-trained VGG19 is fine-tuned with CNN More >

  • Open Access

    ARTICLE

    Face Mask Recognition for Covid-19 Prevention

    Trong Hieu Luu1, Phan Nguyen Ky Phuc2,*, Zhiqiu Yu3, Duy Dung Pham1, Huu Trong Cao1

    CMC-Computers, Materials & Continua, Vol.73, No.2, pp. 3251-3262, 2022, DOI:10.32604/cmc.2022.029663

    Abstract In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask… More >

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