Diagnosis of Cervical Cancer Using Hybrid Multilayered Perceptron (HMLP) Network

Ramli, Dzati Athiar; Kadmin, A. F.; Mashor, Mohd Yusoff; Ashidi, Nor; Isa, Mat

doi:10.1007/978-3-540-30132-5_82

Cited by 8 publications

(6 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The majority of published studies, regarding intelligent systems for cervical cancer support, are concerned about computer aided diagnosis systems based on either cytology or colposcopy image analysis [28–31]. On the other hand, various papers have been published in the past few years concerning bioinformatics' CDSSs based on ANNs for cancer improved detection, treatment, and follow-up support [32–37].…”

Section: Introductionmentioning

confidence: 99%

“…Regarding cervical cancer, an intelligent decision making system may support physicians to improve the selection of protocols for monitoring, diagnosing, and treating women with intraepithelial lesions or cervical cancer or even support the rational selection and the patient-specific follow-up decision making for women who have been treated for high-grade lesions. The majority of published studies, regarding intelligent systems for cervical cancer support, are concerned about computer aided diagnosis systems based on either cytology or colposcopy image analysis [ 28 – 31 ]. On the other hand, various papers have been published in the past few years concerning bioinformatics' CDSSs based on ANNs for cancer improved detection, treatment, and follow-up support [ 32 – 37 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Bountris

Haritou

Pouliakis

et al. 2014

BioMed Research International

View full text Add to dashboard Cite

Nowadays, there are molecular biology techniques providing information related to cervical cancer and its cause: the human Papillomavirus (HPV), including DNA microarrays identifying HPV subtypes, mRNA techniques such as nucleic acid based amplification or flow cytometry identifying E6/E7 oncogenes, and immunocytochemistry techniques such as overexpression of p16. Each one of these techniques has its own performance, limitations and advantages, thus a combinatorial approach via computational intelligence methods could exploit the benefits of each method and produce more accurate results. In this article we propose a clinical decision support system (CDSS), composed by artificial neural networks, intelligently combining the results of classic and ancillary techniques for diagnostic accuracy improvement. We evaluated this method on 740 cases with complete series of cytological assessment, molecular tests, and colposcopy examination. The CDSS demonstrated high sensitivity (89.4%), high specificity (97.1%), high positive predictive value (89.4%), and high negative predictive value (97.1%), for detecting cervical intraepithelial neoplasia grade 2 or worse (CIN2+). In comparison to the tests involved in this study and their combinations, the CDSS produced the most balanced results in terms of sensitivity, specificity, PPV, and NPV. The proposed system may reduce the referral rate for colposcopy and guide personalised management and therapeutic interventions.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Bountris

Haritou

Pouliakis

et al. 2014

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Hierarchical approach: Mehdi et al [103] introduced hierarchical approach to classify images into mild, moderate and severe cells using ANN with back propagation algorithm. Ramli et al [135] approached with a non-linear hybrid multi-layered perception(HMLP) model using least square algorithm to classify cervical cells into normal and low-grade squamous epithelium. Mat-Isa et al [102] also recommended an HMLP network to classify single cell images using different feature informations such as nucleus size, nucleus grey level, cytoplasm size and cytoplasm grey level etc.…”

Section: Classificationmentioning

confidence: 99%

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

Mitra,

Das,

Dey

et al. 2020

Preprint

View full text Add to dashboard Cite

Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions. Automation in cytology started in the early 1950s with the aim to reduce manual efforts in diagnosis of cancer. The inflush of intelligent technological units with high computational power and improved specimen collection techniques helped to achieve its technological heights. In the present survey, we focus on such image processing techniques which put steps forward towards the automation of cytology. We take a short tour to 17 types of cytology and explore various segmentation and/or classification techniques which evolved during last three decades boosting the concept of automation in cytology. It is observed, that most of the works are aligned towards three types of cytology: Cervical, Breast and Lung, which are discussed elaborately in this paper. The user-end systems developed during that period are summarized to comprehend the overall growth in the respective domains. To be precise, we discuss the diversity of the state-of-the-art methodologies, their challenges to provide prolific and competent future research directions inbringing the cytology-based commercial systems into the mainstream.

show abstract

“…In a study by Mango [149], two separate feed-forward neural networks were implemented for independent processing of single-cell and cell-cluster images, outputting an abnormality score. Several neural network (NN) architectures were implemented for this purpose, such as adaptive resonance theory (ART) based [71], RBF [71,150], neural network-relevance vector machine [123], as well as the most common multilayer perception (MLP) [54,151,152], including its hybrid form (HMLP) [153,154]. The latter have also been enhanced in a hierarchical way by having a double HMLP, one for normal/abnormal cell classification and the other to classify the abnormal samples into HSIL or LSIL [129].…”

Section: Literature Review On Computational Approaches For Cervicamentioning

confidence: 99%

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

Conceição

Braga

Rosado

et al. 2019

IJMS

View full text Add to dashboard Cite

Cervical cancer is the one of the most common cancers in women worldwide, affecting around 570,000 new patients each year. Although there have been great improvements over the years, current screening procedures can still suffer from long and tedious workflows and ambiguities. The increasing interest in the development of computer-aided solutions for cervical cancer screening is to aid with these common practical difficulties, which are especially frequent in the low-income countries where most deaths caused by cervical cancer occur. In this review, an overview of the disease and its current screening procedures is firstly introduced. Furthermore, an in-depth analysis of the most relevant computational methods available on the literature for cervical cells analysis is presented. Particularly, this work focuses on topics related to automated quality assessment, segmentation and classification, including an extensive literature review and respective critical discussion. Since the major goal of this timely review is to support the development of new automated tools that can facilitate cervical screening procedures, this work also provides some considerations regarding the next generation of computer-aided diagnosis systems and future research directions.

show abstract

Diagnosis of Cervical Cancer Using Hybrid Multilayered Perceptron (HMLP) Network

Cited by 8 publications

References 18 publications

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

An Intelligent Clinical Decision Support System for Patient-Specific Predictions to Improve Cervical Intraepithelial Neoplasia Detection

Cytology Image Analysis Techniques Towards Automation: Systematically Revisited

A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification

Contact Info

Product

Resources

About