Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

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


    Nuclei Segmentation in Histopathology Images Using Structure-Preserving Color Normalization Based Ensemble Deep Learning Frameworks

    Manas Ranjan Prusty1, Rishi Dinesh2, Hariket Sukesh Kumar Sheth2, Alapati Lakshmi Viswanath2, Sandeep Kumar Satapathy2,3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3077-3094, 2023, DOI:10.32604/cmc.2023.042718

    Abstract This paper presents a novel computerized technique for the segmentation of nuclei in hematoxylin and eosin (H&E) stained histopathology images. The purpose of this study is to overcome the challenges faced in automated nuclei segmentation due to the diversity of nuclei structures that arise from differences in tissue types and staining protocols, as well as the segmentation of variable-sized and overlapping nuclei. To this extent, the approach proposed in this study uses an ensemble of the UNet architecture with various Convolutional Neural Networks (CNN) architectures as encoder backbones, along with stain normalization and test time… More >

  • Open Access


    Recognizing Breast Cancer Using Edge-Weighted Texture Features of Histopathology Images

    Arslan Akram1,2, Javed Rashid2,3,4, Fahima Hajjej5, Sobia Yaqoob1,6, Muhammad Hamid7, Asma Irshad8, Nadeem Sarwar9,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 1081-1101, 2023, DOI:10.32604/cmc.2023.041558

    Abstract Around one in eight women will be diagnosed with breast cancer at some time. Improved patient outcomes necessitate both early detection and an accurate diagnosis. Histological images are routinely utilized in the process of diagnosing breast cancer. Methods proposed in recent research only focus on classifying breast cancer on specific magnification levels. No study has focused on using a combined dataset with multiple magnification levels to classify breast cancer. A strategy for detecting breast cancer is provided in the context of this investigation. Histopathology image texture data is used with the wavelet transform in this… More >

  • Open Access


    A Framework of Deep Learning and Selection-Based Breast Cancer Detection from Histopathology Images

    Muhammad Junaid Umer1, Muhammad Sharif1, Majed Alhaisoni2, Usman Tariq3, Ye Jin Kim4, Byoungchol Chang5,*

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1001-1016, 2023, DOI:10.32604/csse.2023.030463

    Abstract Breast cancer (BC) is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year. The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6% of total cases. Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths. The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis. Manual diagnosis of BC is a complex and challenging task. This work… More >

  • Open Access


    A Stacked Ensemble-Based Classifier for Breast Invasive Ductal Carcinoma Detection on Histopathology Images

    Ali G. Alkhathami*

    Intelligent Automation & Soft Computing, Vol.34, No.1, pp. 235-247, 2022, DOI:10.32604/iasc.2022.024952

    Abstract Breast cancer is one of the main causes of death in women. When body tissues start behaves abnormally and the ratio of tissues growth becomes asymmetrical then this stage is called cancer. Invasive ductal carcinoma (IDC) is the early stage of breast cancer. The early detection and diagnosis of invasive ductal carcinoma is a significant step for the cure of IDC breast cancer. This paper presents a convolutional neural network (CNN) approach to detect and visualize the IDC tissues in breast on histological images dataset. The dataset consists of 90 thousand histopathological images containing two… More >

  • Open Access


    ResNet50-Based Effective Model for Breast Cancer Classification Using Histopathology Images

    Nishant Behar*, Manish Shrivastava

    CMES-Computer Modeling in Engineering & Sciences, Vol.130, No.2, pp. 823-839, 2022, DOI:10.32604/cmes.2022.017030

    Abstract Breast cancer is considered an immense threat and one of the leading causes of mortality in females. It is curable only when detected at an early stage. A standard cancer diagnosis approach involves detection of cancer-related anomalies in tumour histopathology images. Detection depends on the accurate identification of the landmarks in the visual artefacts present in the slide images. Researchers are continuously striving to develop automatic machine-learning algorithms for processing medical images to assist in tumour detection. Nowadays, computer-based automated systems play an important role in cancer image analysis and help healthcare experts make rapid… More >

  • Open Access


    Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder

    Shakra Mehak1, M. Usman Ashraf2, Rabia Zafar3, Ahmed M. Alghamdi4, Ahmed S. Alfakeeh5, Fawaz Alassery6, Habib Hamam7, Muhammad Shafiq8,*

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3407-3423, 2022, DOI:10.32604/cmc.2022.022705

    Abstract Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but… More >

  • Open Access


    A Novel Perspective on Histopathology Provides Novel Insights into Surgical Effects in Pulmonary Atresia, Ventricular Septal Defect, and Major Aortopulmonary Collateral Arteries: A Case-Series Study

    Jinyang Liu1, Xianchao Jiang1, Runsi Wang2, Li Li3, Ju Zhao4, Fuxia Yan5, Run Yuan6, Qiang Wang1,*

    Congenital Heart Disease, Vol.16, No.3, pp. 245-254, 2021, DOI:10.32604/CHD.2021.014881

    Abstract Background: Never had literatures characterized the relationship between the property of major aortopulmonary collateral arteries (MAPCAs) and outcomes of selective unifocalization of pulmonary atresia with ventricular septal defects and MAPCAs. Methods: This is a case-series study. Thirteen patients were included. Angiography-based assessment was conducted to determine whether collateral arteries should be unifocalized or treated with intraoperative ligature. Specimens were collected and stained by HE and ET+VG. Results: Twelve patients underwent one-stage unifocalization at a median age of 37 months (range: 6–228 months) and a median weight of 14.0 kg (range: 5.0–49.0 kg), which produced a favorable right… More >

  • Open Access


    Prognostic and Predictive Significance of Eukaryotic Elongation Factor 1D (eEF1D) in Breast Cancer: A Potential Marker of Response to Endocrine Therapy


    Oncologie, Vol.22, No.3, pp. 147-154, 2020, DOI:10.32604/oncologie.2020.014449

    Abstract Components of the protein synthesis machinery are subjected to alterations in cancer cells. eEF1D gene, which lies within the frequently amplified 8q24 locus, is one of the subunits of the human eukaryotic elongation factor complex. This study aimed to evaluate the prognostic and predictive significance of eEF1D in breast cancer using in silico analysis tools. For this purpose, we analyzed genomic alterations of the eEF1D gene using TCGA datasets via cBioPortal. Histopathological analysis was performed on patient tissue images obtained from cBioPortal and the Human Protein Atlas. Survival analysis was carried out using the KM… More >

  • Open Access


    Focal liver lesions following Fontan palliation of single ventricle physiology: A radiology‐pathology case series

    Emily M. Engelhardt1, Andrew T. Trout2,3, Rachel M. Sheridan4, Gruschen R. Veldtman5, Jonathan R. Dillman2,3

    Congenital Heart Disease, Vol.14, No.3, pp. 380-388, 2019, DOI:10.1111/chd.12730

    Abstract Purpose: Patients who have undergone Fontan palliation of single ventricle physiol‐ ogy congenital heart disease are prone to developing focal liver lesions. In our experi‐ ence, the variety of lesions occurring in this population is greater than that described in the literature. The purpose of this study was to describe the breadth of biopsy‐ proven liver lesions in patients post–Fontan palliation of single ventricle physiology cared for at our institution.
    Methods: We retrospectively identified patients who had previously undergone the Fontan operation and had a focal liver lesion biopsied between January 2000 and June 2018. Medical records… More >

  • Open Access


    Histopathological patterns of ovarian lesions: A study of 161 cases

    Abdulkareem Younis SULEIMAN1, Intisar Salim PITY2, Mohammed R MOHAMMED2, Bashar Abduljabar HASSAWI2

    BIOCELL, Vol.43, No.3, pp. 175-181, 2019, DOI:10.32604/biocell.2019.06884

    Abstract Ovarian lesions are commonly encountered pathologies that cannot be categorized clinicoradiologically. Definite diagnosis is of great importance for therapeutic and prognostic purposes. Histopathology gives accurate diagnosis in most cases. Few cases need supportive tests like immunohistochemistry. Objective: to study the histomorphological diversity of ovarian lesions, their age and location in North of Iraq (Mosul and Duhok). Patients and methods: In the period extended from January 2008 to December 2011, 161 cases of ovarian lesions were collected from pathology departments in Azadi General Hospital “Duhok” and Al-Jamhori Teaching Hospital “Mosul”. Automated tissue processor was used More >

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