Breast Cancer Classification with Machine Learning Classifier Techniques

2020 ◽  
Author(s):  
Neha Panwar ◽  
Deviprasad Sharma ◽  
Naina Narang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Classification ◽  
Breast Cancer Classification ◽  
Learning Classifier

scholarly journals A Fully-Integrated Analog Machine Learning Classifier for Breast Cancer Classification

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 515 ◽  
Author(s):  
Sanjeev T. Chandrasekaran ◽  
Ruobing Hua ◽  
Imon Banerjee ◽  
Arindam Sanyal
Keyword(s):  
Breast Cancer ◽  
Design Methodology ◽  
Activation Function ◽  
Cancer Classification ◽  
Common Source ◽  
Fully Integrated ◽  
Breast Cancer Classification ◽  
Learning Classifier ◽  
Analog Machine ◽  
Artificial Neural Network Ann

We propose a fully integrated common-source amplifier based analog artificial neural network (ANN). The performance of the proposed ANN with a custom non-linear activation function is demonstrated on the breast cancer classification task. A hardware-software co-design methodology is adopted to ensure good matching between the software AI model and hardware prototype. A 65 nm prototype of the proposed ANN is fabricated and characterized. The prototype ANN achieves 97% classification accuracy when operating from a 1.1 V supply with an energy consumption of 160 fJ/classification. The prototype consumes 50 μ W power and occupies 0.003 mm 2 die area.

scholarly journals Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

2021 ◽  
Author(s):  
Warda M. Shaban
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Machine Learning ◽  
Deep Neural Network ◽  
Feature Selection Method ◽  
Data Preprocessing ◽  
Cancer Classification ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Breast Cancer Classification

Abstract Breast cancer is one of the most common types of cancer that affects women globally and it is the primary cause of death. Early detection of breast cancer is a vital process that can facilitate appropriate treatment, stop the progression of cancer cells, and reduce morbidity and mortality. Artificial Intelligence (AI) and Machine Learning (ML) are the most popular methods that can be used to detect and classify breast cancer accurately. In this paper, a new strategy for classifying breast cancer using Neutrosophic Techniques (NTs) and machine learning techniques is introduced, which is called Breast Cancer Classification Strategy (BC2S). The proposed BC2S consists of two phases, which are; Data Preprocessing Phase (DP2) and Breast Cancer Classification Phase (BC2P). The main aim of the data preprocessing phase is to; (i) extract features from mammogram images and then remove the outlier items, (ii) select the most effective and informative features from those extracted features using new feature selection method called Efficient Ant Colony Optimization (EACO), and (iii) convert the selected features from classical domain into neutrosophic domain using NTs to give accurate classification through the next classification phase BC2P. The proposed classification model uses Deep Neural Network (DNN) to determine whether the patient is normal or infected with benign or malignant cancer. According to experimental results, the proposed strategy outperforms other competitors in terms of accuracy, precision, recall, and F-measure.

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