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  • 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 of diabetes and verify the… More >

  • Open Access

    ARTICLE

    Automated Video Generation of Moving Digits from Text Using Deep Deconvolutional Generative Adversarial Network

    Anwar Ullah1, Xinguo Yu1,*, Muhammad Numan2

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2359-2383, 2023, DOI:10.32604/cmc.2023.041219

    Abstract Generating realistic and synthetic video from text is a highly challenging task due to the multitude of issues involved, including digit deformation, noise interference between frames, blurred output, and the need for temporal coherence across frames. In this paper, we propose a novel approach for generating coherent videos of moving digits from textual input using a Deep Deconvolutional Generative Adversarial Network (DD-GAN). The DD-GAN comprises a Deep Deconvolutional Neural Network (DDNN) as a Generator (G) and a modified Deep Convolutional Neural Network (DCNN) as a Discriminator (D) to ensure temporal coherence between adjacent frames. The proposed research involves several steps.… More >

  • Open Access

    ARTICLE

    Convolutional Neural Network Model for Fire Detection in Real-Time Environment

    Abdul Rehman, Dongsun Kim*, Anand Paul

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 2289-2307, 2023, DOI:10.32604/cmc.2023.036435

    Abstract Disasters such as conflagration, toxic smoke, harmful gas or chemical leakage, and many other catastrophes in the industrial environment caused by hazardous distance from the peril are frequent. The calamities are causing massive fiscal and human life casualties. However, Wireless Sensors Network-based adroit monitoring and early warning of these dangerous incidents will hamper fiscal and social fiasco. The authors have proposed an early fire detection system uses machine and/or deep learning algorithms. The article presents an Intelligent Industrial Monitoring System (IIMS) and introduces an Industrial Smart Social Agent (ISSA) in the Industrial SIoT (ISIoT) paradigm. The proffered ISSA empowers smart… More >

  • Open Access

    ARTICLE

    Prediction of Porous Media Fluid Flow with Spatial Heterogeneity Using Criss-Cross Physics-Informed Convolutional Neural Networks

    Jiangxia Han1,2, Liang Xue1,2,*, Ying Jia3, Mpoki Sam Mwasamwasa1,2, Felix Nanguka4, Charles Sangweni5, Hailong Liu3, Qian Li3

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1323-1340, 2024, DOI:10.32604/cmes.2023.031093

    Abstract Recent advances in deep neural networks have shed new light on physics, engineering, and scientific computing. Reconciling the data-centered viewpoint with physical simulation is one of the research hotspots. The physics-informed neural network (PINN) is currently the most general framework, which is more popular due to the convenience of constructing NNs and excellent generalization ability. The automatic differentiation (AD)-based PINN model is suitable for the homogeneous scientific problem; however, it is unclear how AD can enforce flux continuity across boundaries between cells of different properties where spatial heterogeneity is represented by grid cells with different physical properties. In this work,… More >

  • Open Access

    ARTICLE

    An Intelligent Sensor Data Preprocessing Method for OCT Fundus Image Watermarking Using an RCNN

    Jialun Lin1, Qiong Chen1,2,3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1549-1561, 2024, DOI:10.32604/cmes.2023.029631

    Abstract Watermarks can provide reliable and secure copyright protection for optical coherence tomography (OCT) fundus images. The effective image segmentation is helpful for promoting OCT image watermarking. However, OCT images have a large amount of low-quality data, which seriously affects the performance of segmentation methods. Therefore, this paper proposes an effective segmentation method for OCT fundus image watermarking using a rough convolutional neural network (RCNN). First, the rough-set-based feature discretization module is designed to preprocess the input data. Second, a dual attention mechanism for feature channels and spatial regions in the CNN is added to enable the model to adaptively select… More >

  • Open Access

    ARTICLE

    Fast and Accurate Detection of Masked Faces Using CNNs and LBPs

    Sarah M. Alhammad1, Doaa Sami Khafaga1,*, Aya Y. Hamed2, Osama El-Koumy3, Ehab R. Mohamed3, Khalid M. Hosny3

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2939-2952, 2023, DOI:10.32604/csse.2023.041011

    Abstract Face mask detection has several applications, including real-time surveillance, biometrics, etc. Identifying face masks is also helpful for crowd control and ensuring people wear them publicly. With monitoring personnel, it is impossible to ensure that people wear face masks; automated systems are a much superior option for face mask detection and monitoring. This paper introduces a simple and efficient approach for masked face detection. The architecture of the proposed approach is very straightforward; it combines deep learning and local binary patterns to extract features and classify them as masked or unmasked. The proposed system requires hardware with minimal power consumption… More >

  • Open Access

    ARTICLE

    Enhanced 3D Point Cloud Reconstruction for Light Field Microscopy Using U-Net-Based Convolutional Neural Networks

    Shariar Md Imtiaz1, Ki-Chul Kwon1, F. M. Fahmid Hossain1, Md. Biddut Hossain1, Rupali Kiran Shinde1, Sang-Keun Gil2, Nam Kim1,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2921-2937, 2023, DOI:10.32604/csse.2023.040205

    Abstract This article describes a novel approach for enhancing the three-dimensional (3D) point cloud reconstruction for light field microscopy (LFM) using U-net architecture-based fully convolutional neural network (CNN). Since the directional view of the LFM is limited, noise and artifacts make it difficult to reconstruct the exact shape of 3D point clouds. The existing methods suffer from these problems due to the self-occlusion of the model. This manuscript proposes a deep fusion learning (DL) method that combines a 3D CNN with a U-Net-based model as a feature extractor. The sub-aperture images obtained from the light field microscopy are aligned to form… More >

  • Open Access

    ARTICLE

    Application of the Deep Convolutional Neural Network for the Classification of Auto Immune Diseases

    Fayaz Muhammad1, Jahangir Khan1, Asad Ullah1, Fasee Ullah1, Razaullah Khan2, Inayat Khan2, Mohammed ElAffendi3, Gauhar Ali3,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 647-664, 2023, DOI:10.32604/cmc.2023.038748

    Abstract IIF (Indirect Immune Florescence) has gained much attention recently due to its importance in medical sciences. The primary purpose of this work is to highlight a step-by-step methodology for detecting autoimmune diseases. The use of IIF for detecting autoimmune diseases is widespread in different medical areas. Nearly 80 different types of autoimmune diseases have existed in various body parts. The IIF has been used for image classification in both ways, manually and by using the Computer-Aided Detection (CAD) system. The data scientists conducted various research works using an automatic CAD system with low accuracy. The diseases in the human body… More >

  • Open Access

    REVIEW

    Action Recognition and Detection Based on Deep Learning: A Comprehensive Summary

    Yong Li1,4, Qiming Liang2,*, Bo Gan3, Xiaolong Cui4

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1-23, 2023, DOI:10.32604/cmc.2023.042494

    Abstract Action recognition and detection is an important research topic in computer vision, which can be divided into action recognition and action detection. At present, the distinction between action recognition and action detection is not clear, and the relevant reviews are not comprehensive. Thus, this paper summarized the action recognition and detection methods and datasets based on deep learning to accurately present the research status in this field. Firstly, according to the way that temporal and spatial features are extracted from the model, the commonly used models of action recognition are divided into the two stream models, the temporal models, the… More >

  • Open Access

    ARTICLE

    A Double-Branch Xception Architecture for Acute Hemorrhage Detection and Subtype Classification

    Muhammad Naeem Akram1, Muhammad Usman Yaseen1, Muhammad Waqar1, Muhammad Imran1,*, Aftab Hussain2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3727-3744, 2023, DOI:10.32604/cmc.2023.041855

    Abstract This study presents a deep learning model for efficient intracranial hemorrhage (ICH) detection and subtype classification on non-contrast head computed tomography (CT) images. ICH refers to bleeding in the skull, leading to the most critical life-threatening health condition requiring rapid and accurate diagnosis. It is classified as intra-axial hemorrhage (intraventricular, intraparenchymal) and extra-axial hemorrhage (subdural, epidural, subarachnoid) based on the bleeding location inside the skull. Many computer-aided diagnoses (CAD)-based schemes have been proposed for ICH detection and classification at both slice and scan levels. However, these approaches perform only binary classification and suffer from a large number of parameters, which… More >

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