Deep hybrid architectures for binary classification of medical breast cancer images

2022 ◽  
Vol 71 ◽  
pp. 103226
Author(s):  
Hasnae Zerouaoui ◽  
Ali Idri
Keyword(s):  
Breast Cancer ◽  
Binary Classification ◽  
Hybrid Architectures

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.

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.

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.

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 Improving Land Cover Classification Using Genetic Programming for Feature Construction

2021 ◽  
Vol 13 (9) ◽  
pp. 1623
Author(s):  
João E. Batista ◽  
Ana I. R. Cabral ◽  
Maria J. P. Vasconcelos ◽  
Leonardo Vanneschi ◽  
Sara Silva
Keyword(s):  
Land Cover ◽  
Genetic Programming ◽  
Satellite Images ◽  
State Of The Art ◽  
Binary Classification ◽  
Feature Construction ◽  
Classification Problems ◽  
Construction Methods ◽  
Box Models

Genetic programming (GP) is a powerful machine learning (ML) algorithm that can produce readable white-box models. Although successfully used for solving an array of problems in different scientific areas, GP is still not well known in the field of remote sensing. The M3GP algorithm, a variant of the standard GP algorithm, performs feature construction by evolving hyperfeatures from the original ones. In this work, we use the M3GP algorithm on several sets of satellite images over different countries to create hyperfeatures from satellite bands to improve the classification of land cover types. We add the evolved hyperfeatures to the reference datasets and observe a significant improvement of the performance of three state-of-the-art ML algorithms (decision trees, random forests, and XGBoost) on multiclass classifications and no significant effect on the binary classifications. We show that adding the M3GP hyperfeatures to the reference datasets brings better results than adding the well-known spectral indices NDVI, NDWI, and NBR. We also compare the performance of the M3GP hyperfeatures in the binary classification problems with those created by other feature construction methods such as FFX and EFS.

scholarly journals Monotonic Functions Method and Its Application to Staging of Patients with Prostate Cancer According to Pretreatment Data

2021 ◽  
Vol 11 (9) ◽  
pp. 3836
Author(s):  
Valeri Gitis ◽  
Alexander Derendyaev ◽  
Konstantin Petrov ◽  
Eugene Yurkov ◽  
Sergey Pirogov ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Learning Algorithm ◽  
Binary Classification ◽  
Preoperative Staging ◽  
Threshold Value ◽  
Logical Function ◽  
Adequate Treatment ◽  
Monotonic Functions ◽  
Selection Of

Prostate cancer is the second most frequent malignancy (after lung cancer). Preoperative staging of PCa is the basis for the selection of adequate treatment tactics. In particular, an urgent problem is the classification of indolent and aggressive forms of PCa in patients with the initial stages of the tumor process. To solve this problem, we propose to use a new binary classification machine-learning method. The proposed method of monotonic functions uses a model in which the disease’s form is determined by the severity of the patient’s condition. It is assumed that the patient’s condition is the easier, the less the deviation of the indicators from the normal values inherent in healthy people. This assumption means that the severity (form) of the disease can be represented by monotonic functions from the values of the deviation of the patient’s indicators beyond the normal range. The method is used to solve the problem of classifying patients with indolent and aggressive forms of prostate cancer according to pretreatment data. The learning algorithm is nonparametric. At the same time, it allows an explanation of the classification results in the form of a logical function. To do this, you should indicate to the algorithm either the threshold value of the probability of successful classification of patients with an indolent form of PCa, or the threshold value of the probability of misclassification of patients with an aggressive form of PCa disease. The examples of logical rules given in the article show that they are quite simple and can be easily interpreted in terms of preoperative indicators of the form of the disease.

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