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Search Results (8)
  • Open Access


    Improvement method for cervical cancer detection: A comparative analysis


    Oncology Research, Vol.29, No.5, pp. 365-376, 2021, DOI:10.32604/or.2022.025897

    Abstract Cervical cancer is a prevalent and deadly cancer that affects women all over the world. It affects about 0.5 million women anually and results in over 0.3 million fatalities. Diagnosis of this cancer was previously done manually, which could result in false positives or negatives. The researchers are still contemplating how to detect cervical cancer automatically and how to evaluate Pap smear images. Hence, this paper has reviewed several detection methods from the previous researches that has been done before. This paper reviews pre-processing, detection method framework for nucleus detection, and analysis performance of the… More >

  • Open Access


    Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach

    Hiam Alquran1,2, Wan Azani Mustafa3,4,*, Isam Abu Qasmieh2, Yasmeen Mohd Yacob3,4, Mohammed Alsalatie5, Yazan Al-Issa6, Ali Mohammad Alqudah2

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5117-5134, 2022, DOI:10.32604/cmc.2022.025692

    Abstract Cervical cancer is screened by pap smear methodology for detection and classification purposes. Pap smear images of the cervical region are employed to detect and classify the abnormality of cervical tissues. In this paper, we proposed the first system that it ables to classify the pap smear images into a seven classes problem. Pap smear images are exploited to design a computer-aided diagnoses system to classify the abnormality in cervical images cells. Automated features that have been extracted using ResNet101 are employed to discriminate seven classes of images in Support Vector Machine (SVM) classifier. The… 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 More >

  • Open Access


    Intelligent Classification Model for Biomedical Pap Smear Images on IoT Environment

    CSS Anupama1, T. J. Benedict Jose2, Heba F. Eid3, Nojood O Aljehane4, Fahd N. Al-Wesabi5,*, Marwa Obayya6, Anwer Mustafa Hilal7

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3969-3983, 2022, DOI:10.32604/cmc.2022.022701

    Abstract Biomedical images are used for capturing the images for diagnosis process and to examine the present condition of organs or tissues. Biomedical image processing concepts are identical to biomedical signal processing, which includes the investigation, improvement, and exhibition of images gathered using x-ray, ultrasound, MRI, etc. At the same time, cervical cancer becomes a major reason for increased women's mortality rate. But cervical cancer is an identified at an earlier stage using regular pap smear images. In this aspect, this paper devises a new biomedical pap smear image classification using cascaded deep forest (BPSIC-CDF) model… More >

  • Open Access


    Optimal Deep Convolution Neural Network for Cervical Cancer Diagnosis Model

    Mohamed Ibrahim Waly1, Mohamed Yacin Sikkandar1, Mohamed Abdelkader Aboamer1, Seifedine Kadry2, Orawit Thinnukool3,*

    CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 3295-3309, 2022, DOI:10.32604/cmc.2022.020713

    Abstract Biomedical imaging is an effective way of examining the internal organ of the human body and its diseases. An important kind of biomedical image is Pap smear image that is widely employed for cervical cancer diagnosis. Cervical cancer is a vital reason for increased women’s mortality rate. Proper screening of pap smear images is essential to assist the earlier identification and diagnostic process of cervical cancer. Computer-aided systems for cancerous cell detection need to be developed using deep learning (DL) approaches. This study introduces an intelligent deep convolutional neural network for cervical cancer detection and… More >

  • Open Access


    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

    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


    Nucleus Detection on Pap Smear Images for Cervical Cancer Diagnosis: A Review Analysis

    Afiqah Halim1, Wan Azani Mustafa1,2,*, Wan Khairunizam Wan Ahmad1, Hasliza A. Rahim2, Hamzah Sakeran3

    Oncologie, Vol.23, No.1, pp. 73-88, 2021, DOI:10.32604/Oncologie.2021.015154

    Abstract Cervical cancer is a cell disease in the cervix that develops out of control in the female body. The cervix links the vagina (birth canal) with the upper section of the uterus, which can only be found in the female body. This is the second leading cause of death among women around the world. However, cervical cancer is currently one of the most preventable cancers if early detection is identified. The effect of unidentified cancer may increase the risk of death when the cell disease spreads to other parts of the female anatomy (metastasize). The… More >

  • Open Access


    A Narrative Review: Classification of Pap Smear Cell Image for Cervical Cancer Diagnosis

    Wan Azani Mustafa1,*, Afiqah Halim1, Khairul Shakir Ab Rahman2

    Oncologie, Vol.22, No.2, pp. 53-63, 2020, DOI:10.32604/oncologie.2020.013660

    Abstract Cervical cancer develops as cells transformation in the cervix of a female that connects the uterus to the vagina. This cancer may impact the columnal epithelial cells of the cervix and therefore can be expanded to the lymphatic and circulatory system (metastasize), sometimes the kidneys, liver, prostate, vagina, and rectum. Many of the cervical cancer patients survived by taking early prevention by undergoing a Pap Smear Test. However, the result of the test usually takes a few weeks which is extremely time-consuming especially at the government hospital. The purpose of this research was to study… More >

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