Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions

Price, Glynis; McCluggage, W. Glenn; Morrison, Mike; McClean, Gavin; Venkatraman, Lakshmi; Diamond, James J.; Bharucha, H.; Montironi, Rodolfo; Bartels, Peter H.; Thompson, Deborah; Hamilton, Peter

doi:10.1016/s0046-8177(03)00421-0

Cited by 32 publications

(28 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some of the pathological diagnostic clues for CIN are: pleomorphism, superficial maturation and loss of polarity [32]. These can be quantitatively assessed by measuring tissue-level and cellular-level changes in squamous epithelium.…”

Section: B Segmentation Of Nucleimentioning

confidence: 99%

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Wang

Crookes

Eldin

et al. 2009

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

Section: B Segmentation Of Nucleimentioning

confidence: 99%

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Wang

Crookes

Eldin

et al. 2009

IEEE J. Sel. Top. Signal Process.

View full text Add to dashboard Cite

“…They are thus regarded as conditionally independent, hence the definition of ‘naive’. An example of the classification problems of these naïve Bayesian networks is the article published by Price et al on the classification of cervical cancer patients (1). …”

Section: Introductionmentioning

confidence: 99%

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

2016

View full text Add to dashboard Cite

Introduction:Naive Bayesian networks (NBNs) are one of the most effective and simplest Bayesian networks for prediction.Objective:This paper aims to review published evidence about the application of NBNs in predicting disease and it tries to show NBNs as the fundamental algorithm for the best performance in comparison with other algorithms.Methods:PubMed was electronically checked for articles published between 2005 and 2015. For characterizing eligible articles, a comprehensive electronic searching method was conducted. Inclusion criteria were determined based on NBN and its effects on disease prediction. A total of 99 articles were found. After excluding the duplicates (n= 5), the titles and abstracts of 94 articles were skimmed according to the inclusion criteria. Finally, 38 articles remained. They were reviewed in full text and 15 articles were excluded. Eventually, 23 articles were selected which met our eligibility criteria and were included in this study.Result:In this article, the use of NBN in predicting diseases was described. Finally, the results were reported in terms of Accuracy, Sensitivity, Specificity and Area under ROC curve (AUC). The last column in Table 2 shows the differences between NBNs and other algorithms.Discussion:This systematic review (23 studies, 53,725 patients) indicates that predicting diseases based on a NBN had the best performance in most diseases in comparison with the other algorithms. Finally in most cases NBN works better than other algorithms based on the reported accuracy.Conclusion:The method, termed NBNs is proposed and can efficiently construct a prediction model for disease.

show abstract

“…Generally, noise in histology images can be attributed to debris of nuclei, blood cells and artefact stains in the background [25]. Pre-processing is required to reduce the noise and improve the quality of the image in order to determine the ROI.…”

Section: Image Pre-processingmentioning

confidence: 99%

Computer aided decision support system for cervical cancer classification

et al. 2012

View full text Add to dashboard Cite

Conventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the pathologists in their examination of the cervical cancer biopsies. The main aim of the proposed CADSS system is to identify abnormalities and quantify cancer grading in a systematic and repeatable manner. The paper proposes three different methods which presents and compares the results using 475 images of cervical biopsies which include normal, three stages of pre cancer, and malignant cases. This paper will explore various components of an effective CADSS; image acquisition, pre-processing, segmentation, feature extraction, classification, grading and disease identification. Cervical histological images are captured using a digital microscope. The images are captured in sufficient resolution to retain enough information for effective classification. Histology images of cervical biopsies consist of three major sections; background, stroma and squamous epithelium. Most diagnostic information are contained within the epithelium region. This paper will present two levels of segmentations; global (macro) and local (micro). At the global level the squamous epithelium is separated from the background and stroma. At the local or cellular level, the nuclei and cytoplasm are segmented for further analysis. Image features that influence the pathologists' decision during the analysis and classification of a cervical biopsy are the nuclei's shape and spread; the ratio of the areas of nuclei and cytoplasm as well as the texture and spread of the abnormalities. Similar features are extracted towards the automated classification process. This paper will present various feature extraction methods including colour, shape and texture using Gabor wavelet as well as various quantative metrics. Generated features are used to classify cells or regions into normal and abnormal categories. Following the classification process, the cancer is graded based on the spread of the abnormal cells. This paper will present the results of the grading process with five stages of the cancer spectrum. ABSTRACTConventional analysis of a cervical histology image, such a pap smear or a biopsy sample, is performed by an expert pathologist manually. This involves inspecting the sample for cellular level abnormalities and determining the spread of the abnormalities. Cancer is graded based on the spread of the abnormal cells. This is a tedious, subjective and timeconsuming process with considerable variations in diagnosis between the experts. This paper presents a computer aided decision support system (CADSS) tool to help the patholo...

show abstract

Computerized diagnostic decision support system for the classification of preinvasive cervical squamous lesions

Cited by 32 publications

References 25 publications

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Assisted Diagnosis of Cervical Intraepithelial Neoplasia (CIN)

Applying Naive Bayesian Networks to Disease Prediction: a Systematic Review

Computer aided decision support system for cervical cancer classification

Contact Info

Product

Resources

About