Classification of static infrared images using pre-trained CNN for breast cancer detection

2021 ◽  
Author(s):  
Caroline B. Goncalves ◽  
Jefferson R. Souza ◽  
Henrique Fernandes
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Infrared Images

Evaluation of transfer learning of pre-trained CNNs applied to breast cancer detection on infrared images

2020 ◽  
Vol 59 (17) ◽  
pp. E23 ◽  
Author(s):  
Esdras Chaves ◽  
Caroline B. Gonçalves ◽  
Marcelo K. Albertini ◽  
Soojeong Lee ◽  
Gwanggil Jeon ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Transfer Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Infrared Images

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines

2020 ◽  
Vol 16 (6) ◽  
pp. 703-710 ◽  
Author(s):  
Jebasonia Jebamony ◽  
Dheeba Jacob
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Misclassification Rate ◽  
Support Vector ◽  
Mass Detection ◽  
Training Process ◽  
Vector Machines

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

Preprocessing for classification of thermograms in breast cancer detection

2016 ◽  
Author(s):  
Łukasz Neumann ◽  
Robert M. Nowak ◽  
Rafał Okuniewski ◽  
Witold Oleszkiewicz ◽  
Paweł Cichosz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection

Asymmetry features for classification of thermograms in breast cancer detection

2016 ◽  
Author(s):  
Robert M. Nowak ◽  
Rafał Okuniewski ◽  
Witold Oleszkiewicz ◽  
Paweł Cichosz ◽  
Dariusz Jagodziński ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Breast Cancer Detection

scholarly journals Evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women: a cross-sectional study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhongtao Bao ◽  
Yanchun Zhao ◽  
Shuqiang Chen ◽  
Xiaoyu Chen ◽  
Xiang Xu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Detection ◽  
Chinese Women ◽  
Cross Sectional Study ◽  
Breast Cancer Detection ◽  
Sectional Study ◽  
Level Of Evidence ◽  
Cross Sectional ◽  
Asymptomatic Women

Abstract Background Screening of breast cancer in asymptomatic women is important to evaluate for early diagnosis. In China ultrasound is a more frequently used method than mammography for the detection of breast cancer. The objectives of the study were to provide evidence and assessment of parenchymal patterns of ultrasonography for breast cancer detection among Chinese women. Methods Breast ultrasound examinations including the parenchymatous pattern of cytopathological confirmed breast cancer (n = 541) and age-matched cytopathological not confirmed breast cancer (n = 849) women were retrospectively reviewed by seven sonographer physicians. According to compositions of ducts, the thickness of the breast, diameter of ducts, fat lobules, and fibro glandular tissues, the breast parenchymatous pattern was categorized into heterogeneous (high percentage of fatty tissues), ductal (the inner diameters of ducts > 50% of the thick mass of the breast), mixed (the inner diameters of ducts was 50% of the thick mass of the breast), and fibrous categories (a dense classification of the breast). Results Heterogeneous (p < 0.0001, OR = 3.972) and fibrous categories (p < 0.0001, OR = 2.702) were higher among women who have cytopathological confirmed breast cancer than those who have not cytopathological confirmed breast cancer. The heterogeneous category was high-risk ultrasonographic examination category followed by the fibrous category. Agreements between sonographer physicians for categories of ultrasonic examinations were fair to good (Cohen’s k = 0.591). Conclusions Breast cancer risk in Chinese asymptomatic women differ according to the ultrasonographic breast parenchymal pattern. Level of Evidence: III. Technical efficacy stage: 2.

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