scholarly journals MobileNetV2 Ensemble for Cervical Precancerous Lesions Classification

Processes ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 595 ◽  
Author(s):  
Cătălin Buiu ◽  
Vlad-Rareş Dănăilă ◽  
Cristina Nicoleta Răduţă
Keyword(s):  
Uterine Cervix ◽  
Precancerous Lesions ◽  
Binary Classification ◽  
The United States ◽  
Medical Procedure ◽  
Medical Doctors ◽  
Analysis Framework ◽  
Cervical Precancerous Lesions ◽  
Classification Tasks

Women’s cancers remain a major challenge for many health systems. Between 1991 and 2017, the death rate for all major cancers fell continuously in the United States, excluding uterine cervix and uterine corpus cancers. Together with HPV (Human Papillomavirus) testing and cytology, colposcopy has played a central role in cervical cancer screening. This medical procedure allows physicians to view the cervix at a magnification of up to 10%. This paper presents an automated colposcopy image analysis framework for the classification of precancerous and cancerous lesions of the uterine cervix. This framework is based on an ensemble of MobileNetV2 networks. Our experimental results show that this method achieves accuracies of 83.33% and 91.66% on the four-class and binary classification tasks, respectively. These results are promising for the future use of automatic classification methods based on deep learning as tools to support medical doctors.

scholarly journals Identification of Women for Referral to Colposcopy by Neural Networks: A Preliminary Study Based on LBC and Molecular Biomarkers

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Petros Karakitsos ◽  
Charalampos Chrelias ◽  
Abraham Pouliakis ◽  
George Koliopoulos ◽  
Aris Spathis ◽  
...  
Keyword(s):  
Precancerous Lesions ◽  
Correct Classification ◽  
Test Set ◽  
Dna Test ◽  
Hpv Dna ◽  
Vector Quantizer ◽  
Cervical Precancerous Lesions ◽  
Liquid Based Cytology ◽  
Preliminary Study

Objective of this study is to investigate the potential of the learning vector quantizer neural network (LVQ-NN) classifier on various diagnostic variables used in the modern cytopathology laboratory and to build an algorithm that may facilitate the classification of individual cases. From all women included in the study, a liquid-based cytology sample was obtained; this was tested via HPV DNA test, E6/E7 HPV mRNA test, and p16 immunostaining. The data were classified by the LVQ-NN into two groups: CIN-2 or worse and CIN-1 or less. Half of the cases were used to train the LVQ-NN; the remaining cases (test set) were used for validation. Out of the 1258 cases, cytology identified correctly 72.90% of the CIN-2 or worst cases and 97.37% of the CIN-1 or less cases, with overall accuracy 94.36%. The application of the LVQ-NN on the test set allowed correct classification for 84.62% of the cases with CIN-2 or worse and 97.64% of the cases with CIN-1 or less, with overall accuracy of 96.03%. The use of the LVQ-NN with cytology and the proposed biomarkers improves significantly the correct classification of cervical precancerous lesions and/or cancer and may facilitate diagnosis and patient management.

Bases for Strategy Choice and Strategy Transitions in Binary Classification Tasks

2004 ◽  
Author(s):  
Lyle E. Bourne ◽  
Alice F. Healy ◽  
James A. Kole ◽  
William D. Raymond
Keyword(s):  
Binary Classification ◽  
Strategy Choice ◽  
Classification Tasks

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

scholarly journals Updating OMERACT Core Set of Domains for ANCA-associated Vasculitis: Patient Perspective Using the International Classification of Function, Disability, and Health

2019 ◽  
Vol 46 (10) ◽  
pp. 1415-1420 ◽  
Author(s):  
Nataliya Milman ◽  
Eilish McConville ◽  
Joanna C. Robson ◽  
Annelies Boonen ◽  
Peter Tugwell ◽  
...  
Keyword(s):  
The United States ◽  
International Classification ◽  
Antineutrophil Cytoplasmic Antibodies ◽  
Personal Factors ◽  
Disability And Health ◽  
Individual Interviews ◽  
The United Kingdom ◽  
Anca Associated Vasculitis ◽  
Core Set

Objective.Aspects of antineutrophil cytoplasmic antibodies–associated vasculitis (AAV) prioritized by patients with AAV were described using the International Classification of Function, Disability, and Health (ICF).Methods.Items identified during 14 individual interviews were incorporated into an ICF-based questionnaire administered to participants of 2 vasculitis patient symposia: 36 in the United Kingdom and 63 in the United States.Results.Categories identified as at least “moderately relevant” by ≥ 5% of subjects included 44 body functions, 14 body structures, 35 activities and participation, 31 environmental factors, and 38 personal factors.Conclusion.Identified categories differ from those identified by the current Outcome Measures in Rheumatology (OMERACT) core set and those prioritized by vasculitis experts.

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