Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (11)
  • Open Access


    Deep Learning Framework for the Prediction of Childhood Medulloblastoma

    M. Muthalakshmi1,*, T. Merlin Inbamalar2, C. Chandravathi3, K. Saravanan4

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 735-747, 2023, DOI:10.32604/csse.2023.032449

    Abstract This research work develops new and better prognostic markers for predicting Childhood MedulloBlastoma (CMB) using a well-defined deep learning architecture. A deep learning architecture could be designed using ideas from image processing and neural networks to predict CMB using histopathological images. First, a convolution process transforms the histopathological image into deep features that uniquely describe it using different two-dimensional filters of various sizes. A 10-layer deep learning architecture is designed to extract deep features. The introduction of pooling layers in the architecture reduces the feature dimension. The extracted and dimension-reduced deep features from the arrangement of convolution layers and pooling… More >

  • Open Access


    Covid-19 Diagnosis Using a Deep Learning Ensemble Model with Chest X-Ray Images

    Fuat Türk*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1357-1373, 2023, DOI:10.32604/csse.2023.030772

    Abstract Covid-19 is a deadly virus that is rapidly spread around the world towards the end of the 2020. The consequences of this virus are quite frightening, especially when accompanied by an underlying disease. The novelty of the virus, the constant emergence of different variants and its rapid spread have a negative impact on the control and treatment process. Although the new test kits provide almost certain results, chest X-rays are extremely important to detect the progression and degree of the disease. In addition to the Covid-19 virus, pneumonia and harmless opacity of the lungs also complicate the diagnosis. Considering the… More >

  • Open Access


    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

    Hong’an Li1, Min Zhang1,*, Dufeng Chen2, Jing Zhang1, Meng Yang3, Zhanli Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 779-794, 2023, DOI:10.32604/cmes.2022.022369

    Abstract Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis. To overcome the limitations of the color rendering method based on deep learning, such as poor model stability, poor rendering quality, fuzzy boundaries and crossed color boundaries, we propose a novel hinge-cross-entropy generative adversarial network (HCEGAN). The self-attention mechanism was added and improved to focus on the important information of the image. And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models. In this study, we implement the HCEGAN model for image color rendering… More > Graphic Abstract

    Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

  • Open Access


    Simply Fine-Tuned Deep Learning-Based Classification for Breast Cancer with Mammograms

    Vicky Mudeng1,2, Jin-woo Jeong3, Se-woon Choe1,4,*

    CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4677-4693, 2022, DOI:10.32604/cmc.2022.031046

    Abstract A lump growing in the breast may be referred to as a breast mass related to the tumor. However, not all tumors are cancerous or malignant. Breast masses can cause discomfort and pain, depending on the size and texture of the breast. With an appropriate diagnosis, non-cancerous breast masses can be diagnosed earlier to prevent their cultivation from being malignant. With the development of the artificial neural network, the deep discriminative model, such as a convolutional neural network, may evaluate the breast lesion to distinguish benign and malignant cancers from mammogram breast masses images. This work accomplished breast masses classification… More >

  • Open Access


    Medical Image Analysis Using Deep Learning and Distribution Pattern Matching Algorithm

    Mustafa Musa Jaber1,2,*, Salman Yussof1, Amer S. Elameer3, Leong Yeng Weng1, Sura Khalil Abd2,6, Anand Nayyar4,5

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2175-2190, 2022, DOI:10.32604/cmc.2022.023387

    Abstract Artificial intelligence plays an essential role in the medical and health industries. Deep convolution networks offer valuable services and help create automated systems to perform medical image analysis. However, convolution networks examine medical images effectively; such systems require high computational complexity when recognizing the same disease-affected region. Therefore, an optimized deep convolution network is utilized for analyzing disease-affected regions in this work. Different disease-related medical images are selected and examined pixel by pixel; this analysis uses the gray wolf optimized deep learning network. This method identifies affected pixels by the gray wolf hunting process. The convolution network uses an automatic… More >

  • Open Access


    A Post-Processing Algorithm for Boosting Contrast of MRI Images

    B. Priestly Shan1, O. Jeba Shiney1, Sharzeel Saleem2, V. Rajinikanth3, Atef Zaguia4, Dilbag Singh5,*

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2749-2763, 2022, DOI:10.32604/cmc.2022.023057

    Abstract Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two… More >

  • Open Access


    BMRMIA: A Platform for Radiologists to Systematically Learn Automated Medical Image Analysis by Three Dimensional Medical Decision Support System

    Yankun Cao1, Lina Xu3, Zhi Liu2, Xiaoyan Xiao4, Mingyu Wang5, Qin Li6, Hongji Xu2, Geng Yang6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.2, pp. 851-863, 2022, DOI:10.32604/cmes.2022.018424

    Abstract Contribution: This paper designs a learning and training platform that can systematically help radiologists learn automated medical image analysis technology. The platform can help radiologists master deep learning theories and medical applications such as the three-dimensional medical decision support system, and strengthen the teaching practice of deep learning related courses in hospitals, so as to help doctors better understand deep learning knowledge and improve the efficiency of auxiliary diagnosis. Background: In recent years, deep learning has been widely used in academia, industry, and medicine. An increasing number of companies are starting to recruit a large number of professionals in the… More >

  • Open Access


    Hybrid Active Contour Mammographic Mass Segmentation and Classification

    K. Yuvaraj*, U. S. Ragupathy

    Computer Systems Science and Engineering, Vol.40, No.3, pp. 823-834, 2022, DOI:10.32604/csse.2022.018837

    Abstract This research implements a novel segmentation of mammographic mass. Three methods are proposed, namely, segmentation of mass based on iterative active contour, automatic region growing, and fully automatic mask selection-based active contour techniques. In the first method, iterative threshold is performed for manual cropped preprocessed image, and active contour is applied thereafter. To overcome manual cropping in the second method, an automatic seed selection followed by region growing is performed. Given that the result is only a few images owing to over segmentation, the third method uses a fully automatic active contour. Results of the segmentation techniques are compared with… More >

  • Open Access


    Pseudo Zernike Moment and Deep Stacked Sparse Autoencoder for COVID-19 Diagnosis

    Yu-Dong Zhang1, Muhammad Attique Khan2, Ziquan Zhu3, Shui-Hua Wang4,*

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3145-3162, 2021, DOI:10.32604/cmc.2021.018040

    Abstract (Aim) COVID-19 is an ongoing infectious disease. It has caused more than 107.45 m confirmed cases and 2.35 m deaths till 11/Feb/2021. Traditional computer vision methods have achieved promising results on the automatic smart diagnosis. (Method) This study aims to propose a novel deep learning method that can obtain better performance. We use the pseudo-Zernike moment (PZM), derived from Zernike moment, as the extracted features. Two settings are introducing: (i) image plane over unit circle; and (ii) image plane inside the unit circle. Afterward, we use a deep-stacked sparse autoencoder (DSSAE) as the classifier. Besides, multiple-way data augmentation is chosen… More >

  • Open Access


    Screening of COVID-19 Patients Using Deep Learning and IoT Framework

    Harshit Kaushik1, Dilbag Singh2, Shailendra Tiwari3, Manjit Kaur2, Chang-Won Jeong4, Yunyoung Nam5,*, Muhammad Attique Khan6

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3459-3475, 2021, DOI:10.32604/cmc.2021.017337

    Abstract In March 2020, the World Health Organization declared the coronavirus disease (COVID-19) outbreak as a pandemic due to its uncontrolled global spread. Reverse transcription polymerase chain reaction is a laboratory test that is widely used for the diagnosis of this deadly disease. However, the limited availability of testing kits and qualified staff and the drastically increasing number of cases have hampered massive testing. To handle COVID-19 testing problems, we apply the Internet of Things and artificial intelligence to achieve self-adaptive, secure, and fast resource allocation, real-time tracking, remote screening, and patient monitoring. In addition, we implement a cloud platform for… More >

Displaying 1-10 on page 1 of 11. Per Page  

Share Link