Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (25)
  • Open Access

    ARTICLE

    Effect of Direct Statistical Contrast Enhancement Technique on Document Image Binarization

    Wan Azani Mustafa1,2,*, Haniza Yazid3, Ahmed Alkhayyat4, Mohd Aminudin Jamlos3, Hasliza A. Rahim3

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3549-3564, 2022, DOI:10.32604/cmc.2022.019801 - 27 September 2021

    Abstract Background: Contrast enhancement plays an important role in the image processing field. Contrast correction has performed an adjustment on the darkness or brightness of the input image and increases the quality of the image. Objective: This paper proposed a novel method based on statistical data from the local mean and local standard deviation. Method: The proposed method modifies the mean and standard deviation of a neighbourhood at each pixel and divides it into three categories: background, foreground, and problematic (contrast & luminosity) region. Experimental results from both visual and objective aspects show that the proposed method can… More >

  • Open Access

    ARTICLE

    An Efficient Breast Cancer Detection Framework for Medical Diagnosis Applications

    Naglaa F. Soliman1,2, Naglaa S. Ali2, Mahmoud I. Aly2,3, Abeer D. Algarni1,*, Walid El-Shafai4, Fathi E. Abd El-Samie1,4

    CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1315-1334, 2022, DOI:10.32604/cmc.2022.017001 - 07 September 2021

    Abstract Breast cancer is the most common type of cancer, and it is the reason for cancer death toll in women in recent years. Early diagnosis is essential to handle breast cancer patients for treatment at the right time. Screening with mammography is the preferred examination for breast cancer, as it is available worldwide and inexpensive. Computer-Aided Detection (CAD) systems are used to analyze medical images to detect breast cancer, early. The death rate of cancer patients has decreased by detecting tumors early and having appropriate treatment after operations. Processing of mammogram images has four main… More >

  • Open Access

    ARTICLE

    CNN-Based Forensic Method on Contrast Enhancement with JPEG Post-Processing

    Ziqing Yan1,2, Pengpeng Yang1,2, Rongrong Ni1,2,*, Yao Zhao1,2, Hairong Qi3

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3205-3216, 2021, DOI:10.32604/cmc.2021.020324 - 24 August 2021

    Abstract As one of the most popular digital image manipulations, contrast enhancement (CE) is frequently applied to improve the visual quality of the forged images and conceal traces of forgery, therefore it can provide evidence of tampering when verifying the authenticity of digital images. Contrast enhancement forensics techniques have always drawn significant attention for image forensics community, although most approaches have obtained effective detection results, existing CE forensic methods exhibit poor performance when detecting enhanced images stored in the JPEG format. The detection of forgery on contrast adjustments in the presence of JPEG post processing is… More >

  • Open Access

    ARTICLE

    Denoising Medical Images Using Deep Learning in IoT Environment

    Sujeet More1, Jimmy Singla1, Oh-Young Song2,*, Usman Tariq3, Sharaf Malebary4

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 3127-3143, 2021, DOI:10.32604/cmc.2021.018230 - 24 August 2021

    Abstract Medical Resonance Imaging (MRI) is a noninvasive, nonradioactive, and meticulous diagnostic modality capability in the field of medical imaging. However, the efficiency of MR image reconstruction is affected by its bulky image sets and slow process implementation. Therefore, to obtain a high-quality reconstructed image we presented a sparse aware noise removal technique that uses convolution neural network (SANR_CNN) for eliminating noise and improving the MR image reconstruction quality. The proposed noise removal or denoising technique adopts a fast CNN architecture that aids in training larger datasets with improved quality, and SARN algorithm is used for More >

  • Open Access

    ARTICLE

    Color Contrast Enhancement on Pap Smear Images Using Statistical Analysis

    Nadzirah Nahrawi1, Wan Azani Mustafa2,3,*, Siti Nurul Aqmariah Mohd Kanafiah1, Mohd Yusoff Mashor1

    Intelligent Automation & Soft Computing, Vol.30, No.2, pp. 431-438, 2021, DOI:10.32604/iasc.2021.018635 - 11 August 2021

    Abstract In the conventional cervix cancer diagnosis, the Pap smear sample images are taken by using a microscope,causing the cells to be hazy and afflicted by unwanted noise. The captured microscopic images of Pap smear may suffer from some defects such as blurring or low contrasts. These problems can hide and obscure the important cervical cell morphologies, leading to the risk of false diagnosis. The quality and contrast of the Pap smear images are the primary keys that could affect the diagnosis’ accuracy. The paper's main objective is to propose the best contrast enhancement to eliminate… More >

  • Open Access

    ARTICLE

    A Bi-Histogram Shifting Contrast Enhancement for Color Images

    Lord Amoah1,2,*, Ampofo Twumasi Kwabena3

    Journal of Quantum Computing, Vol.3, No.2, pp. 65-77, 2021, DOI:10.32604/jqc.2021.020734 - 22 June 2021

    Abstract Recent contrast enhancement (CE) methods, with a few exceptions, predominantly focus on enhancing gray-scale images. This paper proposes a bihistogram shifting contrast enhancement for color images based on the RGB (red, green, and blue) color model. The proposed method selects the two highest bins and two lowest bins from the image histogram, performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images. The proposed method simultaneously performs both right histogram shifting (RHS) and left histogram shifting (LHS) in each histogram shifting repetition to embed and… More >

  • Open Access

    ARTICLE

    Gastric Tract Disease Recognition Using Optimized Deep Learning Features

    Zainab Nayyar1, Muhammad Attique Khan1, Musaed Alhussein2, Muhammad Nazir1, Khursheed Aurangzeb2, Yunyoung Nam3,*, Seifedine Kadry4, Syed Irtaza Haider2

    CMC-Computers, Materials & Continua, Vol.68, No.2, pp. 2041-2056, 2021, DOI:10.32604/cmc.2021.015916 - 13 April 2021

    Abstract Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via More >

  • Open Access

    ARTICLE

    Deep Learning and Improved Particle Swarm Optimization Based Multimodal Brain Tumor Classification

    Ayesha Bin T. Tahir1, Muhamamd Attique Khan1, Majed Alhaisoni2, Junaid Ali Khan1, Yunyoung Nam3,*, Shui-Hua Wang4, Kashif Javed5

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 1099-1116, 2021, DOI:10.32604/cmc.2021.015154 - 22 March 2021

    Abstract Background: A brain tumor reflects abnormal cell growth. Challenges: Surgery, radiation therapy, and chemotherapy are used to treat brain tumors, but these procedures are painful and costly. Magnetic resonance imaging (MRI) is a non-invasive modality for diagnosing tumors, but scans must be interpretated by an expert radiologist. Methodology: We used deep learning and improved particle swarm optimization (IPSO) to automate brain tumor classification. MRI scan contrast is enhanced by ant colony optimization (ACO); the scans are then used to further train a pretrained deep learning model, via transfer learning (TL), and to extract features from two More >

  • Open Access

    ARTICLE

    Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

    Rabia Javed1,2, Mohd Shafry Mohd Rahim1, Tanzila Saba3, Suliman Mohamed Fati3, Amjad Rehman3,*, Usman Tariq4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2337-2352, 2021, DOI:10.32604/cmc.2021.014677 - 05 February 2021

    Abstract Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds… More >

  • Open Access

    ARTICLE

    Classification of Positive COVID-19 CT Scans Using Deep Learning

    Muhammad Attique Khan1, Nazar Hussain1, Abdul Majid1, Majed Alhaisoni2, Syed Ahmad Chan Bukhari3, Seifedine Kadry4, Yunyoung Nam5,*, Yu-Dong Zhang6

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2923-2938, 2021, DOI:10.32604/cmc.2021.013191 - 28 December 2020

    Abstract In medical imaging, computer vision researchers are faced with a variety of features for verifying the authenticity of classifiers for an accurate diagnosis. In response to the coronavirus 2019 (COVID-19) pandemic, new testing procedures, medical treatments, and vaccines are being developed rapidly. One potential diagnostic tool is a reverse-transcription polymerase chain reaction (RT-PCR). RT-PCR, typically a time-consuming process, was less sensitive to COVID-19 recognition in the disease’s early stages. Here we introduce an optimized deep learning (DL) scheme to distinguish COVID-19-infected patients from normal patients according to computed tomography (CT) scans. In the proposed method,… More >

Displaying 11-20 on page 2 of 25. Per Page