Fluorescence spectroscopy and chemometrics for classification of breast cancer samples—a feasibility study using extended canonical variates analysis

2007 ◽  
Vol 21 (10-11) ◽  
pp. 451-458 ◽  
Author(s):  
Lars Nørgaard ◽  
György Sölétormos ◽  
Niels Harrit ◽  
Morten Albrechtsen ◽  
Ole Olsen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Fluorescence Spectroscopy ◽  
Feasibility Study ◽  
Canonical Variates Analysis ◽  
Canonical Variates

CHEMICAL PROPERTIES AND CLASSIFICATION OF ORGANIC HORIZONS FROM SELECTED SOILS IN BRITISH COLUMBIA

1987 ◽  
Vol 67 (2) ◽  
pp. 383-394 ◽  
Author(s):  
L. E. LOWE ◽  
A. M. SCAGEL ◽  
K. KLINKA
Keyword(s):  
British Columbia ◽  
Correlation Analysis ◽  
Key Words ◽  
Chemical Properties ◽  
Morphological Characteristics ◽  
Soil Classification ◽  
Canonical Variates Analysis ◽  
Organic Horizons ◽  
Canonical Variates

In order to assess the extent to which chemical properties of organic horizons support their differentiation on the basis of morphological characteristics, as used in the Canadian System of Soil Classification (CSSC), a total of 265 samples of organic horizons (including L, F, H, Of, Om and Oh) were analyzed for 26 chemical properties (organic and inorganic). One-way analysis of variance, correlation analysis and tests of normality were used to select variables for canonical variates analysis (CVA). The results of CVA indicated that the six types of horizon could be distinguished by a combination of chemical properties, and that the grouped upland (L-F-H) horizons could be distinguished from the grouped wetland (Of-Om-Oh) horizons. The results provided support for the present usage in the CSSC, based on field morphology. It was concluded that there remained a need to improve definitions, to review designations, and to develop quantitative diagnostic criteria for organic horizons. Some problems in the classification of organic horizons are discussed, particularly in relation to a hierarchical system. Key words: Organic horizons, horizon classification, British Columbia, chemical properties

Model for Classification of ChEMBL Dataset of Anti-breast cancer Compounds According to Multiple Pharmacological Parameters, Biological Assays, and Receptors

2015 ◽  
Vol 13 (999) ◽  
pp. 1-1
Author(s):  
Francisco J. Prado-Prado ◽  
Angel G. Arguello-Chan ◽  
Coraima I. Estrada-Domínguez ◽  
Alejandra Aguirre-Crespo ◽  
Francisco J. Aguirre-Crespo ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Biological Assays

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

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.

Hormonal characterization and classification of breast cyst fluid in gross cystic mastopathy

1994 ◽  
Vol 1 (2) ◽  
pp. 49-55 ◽  
Author(s):  
I Számel ◽  
B Budai ◽  
K Daubner ◽  
J Kralovánszky ◽  
Sz Ottó ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Sex Steroids ◽  
Linear Regression Analysis ◽  
Cyst Fluid ◽  
Type I ◽  
Breast Cyst Fluid ◽  
Breast Cyst ◽  
Apocrine Metaplasia ◽  
Endocrine Parameters

ABSTRACT Gross cystic disease (GCD) of the breast may be associated with a higher risk for the development of breast cancer. High levels of sex steroids, steroid hormone precursors, prolactin and cations have been found in breast cyst fluid (BCF) by several investigators. Accordingly, endocrine parameters and the cationic composition of BCF may be considered as useful characteristics to follow patients bearing macrocysts. In this study we have investigated the concentrations of estradiol (E2), progesterone, testosterone, dehydroepiandrosterone (DHA) and DHA-3-sulfate (DHA-S), prolactin, potassium (K+) and sodium (Na+) in BCF aspirated from 99 women. The mean age of the patients was 49.8 years (range 32-58). The hormone levels were measured by RIA methods; K+ and Na+ were determined by flame photometry. Estradiol, progesterone, testosterone, DHA, DHA-S, prolactin and K+ showed significant accumulation in the BCF compared with their respective serum values. The K+/Na+ ratio proved to be useful in dividing cysts into type I (≥1), type II (<1 but ≥0.1) and type III (<0.1) subgroups. For type I BCF, higher DHA, DHA-S and prolactin concentrations were detected. Linear regression analysis established a highly significant (P<0.001) correlation between the concentrations of E2 and DHA-S (r=0.686), and also between testosterone and DHA-S (r=0.711). These findings indicate that type I BCF might be a marker for 'active' GCD of the breast, and suggest that it may be associated with an increased breast cancer risk, since this group of patients is supposed to have cysts with apocrine metaplasia. It is suggested therefore that when BCF is aspirated, sex steroids, steroid precursors and cations should be routinely measured, and women with type I cysts should be regularly examined.

scholarly journals Identification of Somatic Mutations in Thirty-year-old Serum Cell-free DNA From Patients With Breast Cancer: A Feasibility Study

2020 ◽  
Vol 20 (5) ◽  
pp. 413-421.e1
Author(s):  
Mathilde Ritter ◽  
Viola Paradiso ◽  
Patrik Widmer ◽  
Andrea Garofoli ◽  
Luca Quagliata ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Feasibility Study ◽  
Somatic Mutations ◽  
Cell Free Dna ◽  
Free Dna
Sign in / Sign up

Export Citation Format

Share Document