scholarly journals Adversities are all you need: Classification of self-reported breast cancer posts on Twitter using Adversarial Fine-tuning

2021 ◽  
Author(s):  
Adarsh Kumar ◽  
Ojasv Kamal ◽  
Susmita Mazumdar
Keyword(s):  
Breast Cancer ◽  
Fine Tuning

scholarly journals Classification of Hematoxylin and Eosin-Stained Breast Cancer Histology Microscopy Images Using Transfer Learning with EfficientNets

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Chanaleä Munien ◽  
Serestina Viriri
Keyword(s):  
Breast Cancer ◽  
Human Error ◽  
Final Diagnosis ◽  
Normalization Method ◽  
Fine Tuning ◽  
In Situ Carcinoma ◽  
Hematoxylin And Eosin ◽  
The Cost ◽  
The Impact

Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

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 Sparse Autoencoder-based Multi-head Deep Neural Networks for Machinery Fault Diagnostics with Detection of Novelties

2021 ◽  
Vol 34 (1) ◽  
Author(s):  
Zhe Yang ◽  
Dejan Gjorgjevikj ◽  
Jianyu Long ◽  
Yanyang Zi ◽  
Shaohui Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Supervised Classification ◽  
Deep Neural Networks ◽  
Activation Function ◽  
Reconstruction Error ◽  
Fine Tuning ◽  
Fault Diagnostics ◽  
Sparse Autoencoder

AbstractSupervised fault diagnosis typically assumes that all the types of machinery failures are known. However, in practice unknown types of defect, i.e., novelties, may occur, whose detection is a challenging task. In this paper, a novel fault diagnostic method is developed for both diagnostics and detection of novelties. To this end, a sparse autoencoder-based multi-head Deep Neural Network (DNN) is presented to jointly learn a shared encoding representation for both unsupervised reconstruction and supervised classification of the monitoring data. The detection of novelties is based on the reconstruction error. Moreover, the computational burden is reduced by directly training the multi-head DNN with rectified linear unit activation function, instead of performing the pre-training and fine-tuning phases required for classical DNNs. The addressed method is applied to a benchmark bearing case study and to experimental data acquired from a delta 3D printer. The results show that its performance is satisfactory both in detection of novelties and fault diagnosis, outperforming other state-of-the-art methods. This research proposes a novel fault diagnostics method which can not only diagnose the known type of defect, but also detect unknown types of defects.

scholarly journals Smart Design Nano-Hybrid Formulations by Machine Learning

Proceedings ◽  
2020 ◽  
Vol 78 (1) ◽  
pp. 5
Author(s):  
Raquel de Melo Barbosa ◽  
Fabio Fonseca de Oliveira ◽  
Gabriel Bezerra Motta Câmara ◽  
Tulio Flavio Accioly de Lima e Moura ◽  
Fernanda Nervo Raffin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Experimental Data ◽  
Water Solubility ◽  
Inorganic Materials ◽  
Fine Tuning ◽  
Support Vector ◽  
Drug Solubility ◽  
Physical Behavior ◽  
Best Fit

Nano-hybrid formulations combine organic and inorganic materials in self-assembled platforms for drug delivery. Laponite is a synthetic clay, biocompatible, and a guest of compounds. Poloxamines are amphiphilic four-armed compounds and have pH-sensitive and thermosensitive properties. The association of Laponite and Poloxamine can be used to improve attachment to drugs and to increase the solubility of β-Lapachone (β-Lap). β-Lap has antiviral, antiparasitic, antitumor, and anti-inflammatory properties. However, the low water solubility of β-Lap limits its clinical and medical applications. All samples were prepared by mixing Tetronic 1304 and LAP in a range of 1–20% (w/w) and 0–3% (w/w), respectively. The β-Lap solubility was analyzed by UV-vis spectrophotometry, and physical behavior was evaluated across a range of temperatures. The analysis of data consisted of response surface methodology (RMS), and two kinds of machine learning (ML): multilayer perceptron (MLP) and support vector machine (SVM). The ML techniques, generated from a training process based on experimental data, obtained the best correlation coefficient adjustment for drug solubility and adequate physical classifications of the systems. The SVM method presented the best fit results of β-Lap solubilization. In silico tools promoted fine-tuning, and near-experimental data show β-Lap solubility and classification of physical behavior to be an excellent strategy for use in developing new nano-hybrid platforms.

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