Classification of Thermography Breast Images for Cancer Detection using Machine Learning

2020 ◽  
pp. 01-06
Author(s):  
Aishwarya Nirkhede ◽  
Ruchika Adkine ◽  
Bhargavi Lohit ◽  
Dipali Lande ◽  
Swita Nitnaware ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Urban Communities ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Rural Areas ◽  
Early Recognition ◽  
Skin Layer ◽  
Temperature Changes ◽  
The Individual

Breast cancer is presently the most well-known cancer in many urban communities in India, and the second generally normal in rural areas. Early recognition of breast cancer by orderly assessment of the individual may improve the endurance rate. Infrared thermography one of the imaging strategies that produce high goals infrared pictures shows the warmth design dependent on the temperature changes in breast regarding the movement of the cancer cells. Expanded metabolic movement and the bloodstream because of the augmentation of cancer cells instigates more warmth on the skin layer which are caught by the warm camera to deliver the thermal images. This paper talks about the picture handling calculation to recognize the nearness of cancer from the procured warm pictures. The approach incorporates the preprocessing the procured picture and fragmenting the area of enthusiasm, extricating the features from the divided picture followed by feature selection and classification.

scholarly journals Detection of Breast Cancer from Thermography Images

2019 ◽  
Vol 8 (6) ◽  
pp. 677-682
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Rural Areas ◽  
Region Of Interest ◽  
Skin Layer ◽  
Systematic Evaluation ◽  
Image Processing Algorithm ◽  
Thermal Images ◽  
Heat Pattern ◽  
The Individual

Breast cancer is now the most common cancer in most cities in India, and the second most common in rural areas. Early detection of breast cancer by systematic evaluation of the individual may improve survival rate. Infrared thermography one of the imaging technique that produce high resolution infrared images shows the heat pattern based on the temperature changesin breast with respect to tshe progression of the cancer cells. Increased metabolic activity and the blood flow due to the multiplication of cancer cells induces more heat on the skin layer which are captured by the thermal camera to produce the thermal images. This paper discuss on the real time image processing algorithm to detect the presence of cancer from the acquired thermal images. The methodology includes the preprocessing the acquired image and segmenting the region of interest, extracting the features from the segmented image followed by feature selection and classification. From the results, it is inferred that ANN classifiers yields better classification accuracy of 92% and minimum error rate (0.08) in K-means segmentation method when compared with SVM, KNN classifiers

scholarly journals Combined Luteolin and Indole-3-Carbinol Synergistically Constrains ERα-Positive Breast Cancer by Dual Inhibiting Estrogen Receptor Alpha and Cyclin-Dependent Kinase 4/6 Pathway in Cultured Cells and Xenograft Mice

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2116
Author(s):  
Xiaoyong Wang ◽  
Lijuan Zhang ◽  
Qi Dai ◽  
Hongzong Si ◽  
Longyun Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cell ◽  
Breast Cancer Cells ◽  
Cultured Cells ◽  
Inhibitory Effect ◽  
Cyclin Dependent Kinase ◽  
Xenograft Mice ◽  
The Individual

The high concentrations of individual phytochemicals in vitro studies cannot be physiologically achieved in humans. Our solution for this concentration gap between in vitro and human studies is to combine two or more phytochemicals. We screened 12 phytochemicals by pairwise combining two compounds at a low level to select combinations exerting the synergistic inhibitory effect of breast cancer cell proliferation. A novel combination of luteolin at 30 μM (LUT30) and indole-3-carbinol 40 μM (I3C40) identified that this combination (L30I40) synergistically constrains ERα+ breast cancer cell (MCF7 and T47D) proliferation only, but not triple-negative breast cancer cells. At the same time, the individual LUT30 and I3C40 do not have this anti-proliferative effect in ERα+ breast cancer cells. Moreover, this combination L30I40 does not have toxicity on endothelial cells compared to the current commercial drugs. Similarly, the combination of LUT and I3C (LUT10 mg + I3C10 mg/kg/day) (IP injection) synergistically suppresses tumor growth in MCF7 cells-derived xenograft mice, but the individual LUT (10 mg/kg/day) and I3C (20 mg/kg/day) do not show an inhibitory effect. This combination synergistically downregulates two major therapeutic targets ERα and cyclin dependent kinase (CDK) 4/6/retinoblastoma (Rb) pathway, both in cultured cells and xenograft tumors. These results provide a solid foundation that a combination of LUT and I3C may be a practical approach to treat ERα+ breast cancer cells after clinical trials.

scholarly journals A rapid nanobiosensing platform based on herceptin-conjugated graphene for ultrasensitive detection of circulating tumor cells in early breast cancer

2021 ◽  
Vol 10 (1) ◽  
pp. 744-753
Author(s):  
Zahra Rahimzadeh ◽  
Seyed Morteza Naghib ◽  
Esfandyar Askari ◽  
Fatemeh Molaabasi ◽  
Ali Sadr ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Breast Cancer Cells ◽  
Graphene Nanosheets ◽  
Her2 Positive ◽  
Promising Candidate ◽  
Graphene Layers ◽  
The Stability ◽  
Positive Breast Cancer

Abstract In this paper, we use a simple and cheap approach for the synthesis of herceptin-conjugated graphene biosensor to detect the HER2-positive breast cancer cells. The bifunctional graphene-herceptin nanosheets are prepared from graphite by a simple ultrasonic-mediated technique. The prepared protein-mediated graphene is fully characterized. The results show the exfoliation of graphene layers in herceptin solution. Moreover, herceptin is effectively conjugated into the surface of graphene nanosheets. The synthesized herceptin-conjugated graphene is applied for breast cancer detection. The linear range of this biosensor is 1–80 cells, which is significant. The biosensor shows an excellent selectivity performance for detection of HER2-positive cancer cells. Likewise, the stability and functionality of the biosensor is about 40 days. Based on the results, this device is a promising candidate for rapid and selective detection of cancer cells.

Mueller polarimetric microscopic images analysis based classification of breast cancer cells

2020 ◽  
Vol 475 ◽  
pp. 126194
Author(s):  
Longyu Xia ◽  
Yue Yao ◽  
Yang Dong ◽  
Mingzhe Wang ◽  
Hui Ma ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Breast Cancer Cells ◽  
Microscopic Images

scholarly journals Breast Cancer Detection via Mammographic Images : A Survey

2020 ◽  
pp. 173-195
Author(s):  
Mary Walowe Mwadulo ◽  
Raphael Angulu ◽  
Stephen Makau Mutua
Keyword(s):  
Breast Cancer ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Breast Cancer Cells ◽  
Cost Effective ◽  
Breast Cancer Detection ◽  
Term Survival ◽  
Mammographic Images ◽  
Cancer Breast ◽  
Future Work

Breast cancer is a top killer disease for women globally. The long term survival rate of women can be improved through early and effective screening of breast cancer cells. Currently, a mammogram is the recommended tool for breast cancer screening since it can identify breast cancer cells several years before physical signs appear and it is cost effective. This paper analyzes mammographic detection of breast cancer by providing an explanation on development and classification of Breast Cancer, Image representation models for breast tumor, mammography technologies, a discussion on various mammographic signs of breast cancer, breast cancer feature extraction techniques, popular breast cancer classification techniques, comparative analysis of existing mammogram breast cancer databases, and a review of mammographic breast cancer detection studies are presented. Finally, a highlight on future work is given.

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.

The Role of Adhesion in Gold Nanoparticles for Cancer Detection and Treatment

2015 ◽  
Vol 1132 ◽  
pp. 72-86
Author(s):  
Y. Oni ◽  
J.D. Obayemi ◽  
K. Kao ◽  
S. Dozie-Nwachukwu ◽  
S. Odusanya ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gold Nanoparticles ◽  
Cancer Cells ◽  
Cancer Detection ◽  
Breast Cancer Cells ◽  
Au Nanoparticle ◽  
Adhesion Forces ◽  
Force Microscopy ◽  
Nanoparticle Clusters ◽  
Potential Applications

This paper presents the results of an experimental study of the effects of adhesion between gold nanoparticles and surfaces that are relevant to the potential applications in cancer detection and treatment. Adhesion is measured using a dip coating/atomic force microscopy (DC/AFM) technique. The adhesion forces are obtained for dip-coated gold nanoparticles that interact with peptide or antibody-based molecular recognition units (MRUs) that attach specifically to breast cancer cells. They include MRUs that attach specifically to receptors on breast cancer cells. Adhesion forces between anti-cancer drugs such as paclitaxel, and the constituents of MRU-conjugated Au nanoparticle clusters, are measured using force microscopy techniques. The implications of the results are then discussed for the design of robust gold nanoparticle clusters and for potential applications in localized drug delivery and hyperthermia.

scholarly journals Breast Cancer Detection Using Combination of Feature Extraction Models

2018 ◽  
Vol 7 (3.12) ◽  
pp. 848
Author(s):  
T Suneetha Rani ◽  
S J Soujanya ◽  
Pole Anjaiah
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Texture Feature ◽  
Region Of Interest ◽  
Computation Time ◽  
Morphological Operations ◽  
Texture Feature Extraction ◽  
Mammogram Images

Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities.  Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation.  This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

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