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Search Results (12)
  • Open Access


    Classification of Multi-view Digital Mammogram Images Using SMO-WkNN

    P. Malathi1,*, G. Charlyn Pushpa Latha2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 1741-1758, 2023, DOI:10.32604/csse.2023.035185

    Abstract Breast cancer (BCa) is a leading cause of death in the female population across the globe. Approximately 2.3 million new BCa cases are recorded globally in females, overtaking lung cancer as the most prevalent form of cancer to be diagnosed. However, the mortality rates for cervical and BCa are significantly higher in developing nations than in developed countries. Early diagnosis is the only option to minimize the risks of BCa. Deep learning (DL)-based models have performed well in image processing in recent years, particularly convolutional neural network (CNN). Hence, this research proposes a DL-based CNN model to diagnose BCa from… More >

  • Open Access


    Automatic Detection and Classification of Insects Using Hybrid FF-GWO-CNN Algorithm

    B. Divya*, M. Santhi

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1881-1898, 2023, DOI:10.32604/iasc.2023.031573

    Abstract Pest detection in agricultural crop fields is the most challenging task, so an effective pest detection technique is required to detect insects automatically. Image processing techniques are widely preferred in agricultural science because they offer multiple advantages like maximal crop protection, improved crop management and productivity. On the other hand, developing the automatic pest monitoring system dramatically reduces the workforce and errors. Existing image processing approaches are limited due to the disadvantages like poor efficiency and less accuracy. Therefore, a successful image processing technique based on FF-GWO-CNN classification algorithm is introduced for effective pest monitoring and detection. The four-step image… More >

  • Open Access


    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 to eliminate salt & pepper… More >

  • Open Access


    Novel Optimized Framework for Video Processing in IoRT Driven Hospitals

    Mani Deepak Choudhry1,*, B. Aruna Devi2, M. Sundarrajan3

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 267-278, 2022, DOI:10.32604/iasc.2022.024024

    Abstract Internet of Remote things (IoRT) has gained recent attention and is considered as one most prominent research topics being carried out by numerous researchers worldwide. IoRT is being used in various applications and this paper mainly concentrates on the healthcare industry wherein it could be used effectively for patient monitoring. IoRT plays a crucial role in monitoring the patients in any healthcare center remotely by allowing simultaneous video transmissions possible from the emergency areas like Intensive Care Unit (ICU). Considering general scenarios, the video transmissions are done by the main use of Gaussian distribution. With the help of the proposed… More >

  • Open Access


    Automatic Segmentation and Detection System for Varicocele Using Ultrasound Images

    Ayman M. Abdalla1,*, Mohammad Abu Awad2, Omar AlZoubi2, La'aly A. Al-Samrraie3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 797-814, 2022, DOI:10.32604/cmc.2022.024913

    Abstract The enlarged veins in the pampiniform venous plexus, known as varicocele disease, are typically identified using ultrasound scans. The medical diagnosis of varicocele is based on examinations made in three positions taken to the right and left testicles of the male patient. The proposed system is designed to determine whether a patient is affected. Varicocele is more frequent on the left side of the scrotum than on the right and physicians commonly depend on the supine position more than other positions. Therefore, the experimental results of this study focused on images taken in the supine position of the left testicles… More >

  • Open Access


    Optimal Deep Learning Based Inception Model for Cervical Cancer Diagnosis

    Tamer AbuKhalil1, Bassam A. Y. Alqaralleh2,*, Ahmad H. Al-Omari3

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 57-71, 2022, DOI:10.32604/cmc.2022.024367

    Abstract Prevention of cervical cancer becomes essential and is carried out by the use of Pap smear images. Pap smear test analysis is laborious and tiresome work performed visually using a cytopathologist. Therefore, automated cervical cancer diagnosis using automated methods are necessary. This paper designs an optimal deep learning based Inception model for cervical cancer diagnosis (ODLIM-CCD) using pap smear images. The proposed ODLIM-CCD technique incorporates median filtering (MF) based pre-processing to discard the noise and Otsu model based segmentation process. Besides, deep convolutional neural network (DCNN) based Inception with Residual Network (ResNet) v2 model is utilized for deriving the feature… More >

  • Open Access


    Video Surveillance-Based Urban Flood Monitoring System Using a Convolutional Neural Network

    R. Dhaya1,*, R. Kanthavel2

    Intelligent Automation & Soft Computing, Vol.32, No.1, pp. 183-192, 2022, DOI:10.32604/iasc.2022.021538

    Abstract The high prevalence of urban flooding in the world is increasing rapidly with the rise in extreme weather events. Consequently, this research uses an Automatic Flood Monitoring System (ARMS) through a video surveillance camera. Initially, videos are collected from a surveillance camera and converted into video frames. After converting the video frames, the water level can be identified by using a Histogram of oriented Gradient (HoG), which is used to remove the functionality. Completing the extracted features, the frames are enhanced by using a median filter to remove the unwanted noise from the image. The next step is water level… More >

  • Open Access


    Analytic Beta-Wavelet Transform-Based Digital Image Watermarking for Secure Transmission

    Hesham Alhumyani1,*, Ibrahim Alrube1, Sameer Alsharif1, Ashraf Afifi1, Chokri Ben Amar1, Hala S. El-Sayed2, Osama S. Faragallah3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4657-4673, 2022, DOI:10.32604/cmc.2022.020338

    Abstract The rapid development in the information technology field has introduced digital watermark technologies as a solution to prevent unauthorized copying and redistribution of data. This article introduces a self-embedded image verification and integrity scheme. The images are firstly split into dedicated segments of the same block sizes. Then, different Analytic Beta-Wavelet (ABW) orthogonal filters are utilized for embedding a self-segment watermark for image segment using a predefined method. ABW orthogonal filter coefficients are estimated to improve image reconstruction under different block sizes. We conduct a comparative study comparing the watermarked images using three kinds of ABW filters for block sizes… More >

  • Open Access


    Adaptive Image Enhancement Using Hybrid Particle Swarm Optimization and Watershed Segmentation

    N. Mohanapriya1, Dr. B. Kalaavathi2

    Intelligent Automation & Soft Computing, Vol.25, No.4, pp. 663-672, 2019, DOI:10.31209/2018.100000041

    Abstract Medical images are obtained straight from the medical acquisition devices so that, the image quality becomes poor and may contain noises. Low contrast and poor quality are the major issues in the production of medical images. Medical imaging enhancement technology gives way to solve these issues; it helps the doctors to see the interior portions of the body for early diagnosis, also it improves the features the visual aspects of an image for a right diagnosis. This paper proposes a new blend of Particle Swarm Optimization (PSO) and Accelerated Particle Swarm Optimization (APSO) called Hybrid Partial Swarm Optimization (HPSO) to… More >

  • Open Access


    Median Filtering Detection Based on Quaternion Convolutional Neural Network

    Jinwei Wang1, 2, 3, 4, Qiye Ni3, Yang Zhang3, Xiangyang Luo2, *, Yunqing Shi5, Jiangtao Zhai3, Sunil Kr Jha3

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 929-943, 2020, DOI:10.32604/cmc.2020.06569

    Abstract Median filtering is a nonlinear signal processing technique and has an advantage in the field of image anti-forensics. Therefore, more attention has been paid to the forensics research of median filtering. In this paper, a median filtering forensics method based on quaternion convolutional neural network (QCNN) is proposed. The median filtering residuals (MFR) are used to preprocess the images. Then the output of MFR is expanded to four channels and used as the input of QCNN. In QCNN, quaternion convolution is designed that can better mix the information of different channels than traditional methods. The quaternion pooling layer is designed… More >

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