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.

scholarly journals Prediction of Breast Cancer images Classification Using Bidirectional Long Short Term Memory and Two-Dimensional Convolutional Neural network

2021 ◽  
Vol 9 (4) ◽  
pp. 29-38
Author(s):  
Oluwashola David Adeniji
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Screening Methods ◽  
Cancer Treatments ◽  
The Neural Network ◽  
Histopathological Images ◽  
Long Short Term Memory

Breast cancer is most prevalent among women around the world and Nigeria is no exception in this menace. The increased in survival rate is due to the dramatic advancement in the screening methods, early diagnosis, and discovery in cancer treatments. There is an improvement in different strategies of breast cancer classification. A model for   training   deep   neural networks   for classification   of   breast   cancer in histopathological images was developed in this study. However, this images are affected by data unbalance with the support of active learning. The output of the neural network on unlabeled samples was used to calculate weighted information entropy. It is utilized as uncertainty score for automatic selecting both samples with high and low confidence. A threshold   that   decays over iteration number is used   to   decide which high confidence samples should be concatenated with manually labeled samples and then used infine-tuning of convolutional neural network. The neural network was optionally trained using weighted cross-entropy loss to better cope with bias towards the majority class. The developed model was compared with the existing model. The accuracy level of 98.3% was achieved for the developed model while the existing model 93.97%. The accuracy gain of 4.33%. was achieved as performance in the prediction of breast cancer .  

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.

Detection and Classification of Brain Tumors from MRI Images Using a Deep Convolutional Neural Network Approach

2022 ◽  
Vol 10 (1) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Deep Convolutional Neural Network ◽  
Neural Network Models ◽  
Neural Network Approach ◽  
Cancer Disease

Brain tumor is a severe cancer disease caused by uncontrollable and abnormal partitioning of cells. Timely disease detection and treatment plans lead to the increased life expectancy of patients. Automated detection and classification of brain tumor are a more challenging process which is based on the clinician’s knowledge and experience. For this fact, one of the most practical and important techniques is to use deep learning. Recent progress in the fields of deep learning has helped the clinician’s in medical imaging for medical diagnosis of brain tumor. In this paper, we present a comparison of Deep Convolutional Neural Network models for automatically binary classification query MRI images dataset with the goal of taking precision tools to health professionals based on fined recent versions of DenseNet, Xception, NASNet-A, and VGGNet. The experiments were conducted using an MRI open dataset of 3,762 images. Other performance measures used in the study are the area under precision, recall, and specificity.

scholarly journals Classification of breast cancer cytological specimen using convolutional neural network

2017 ◽  
Vol 783 ◽  
pp. 012060 ◽  
Author(s):  
Michał Żejmo ◽  
Marek Kowal ◽  
Józef Korbicz ◽  
Roman Monczak
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Cytological Specimen
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