scholarly journals Artificial neural network with Taguchi method for robust classification model to improve classification accuracy of breast cancer

2021 ◽  
Vol 7 ◽  
pp. e344
Author(s):  
Md Akizur Rahman ◽  
Ravie chandren Muniyandi ◽  
Dheeb Albashish ◽  
Md Mokhlesur Rahman ◽  
Opeyemi Lateef Usman
Keyword(s):  
Breast Cancer ◽  
Taguchi Method ◽  
Cancer Classification ◽  
Classification Model ◽  
Breast Cancer Dataset ◽  
Robust Classification ◽  
Breast Cancer Classification ◽  
Proposed Model ◽  
Artificial Neural ◽  
Hidden Layer

Artificial neural networks (ANN) perform well in real-world classification problems. In this paper, a robust classification model using ANN was constructed to enhance the accuracy of breast cancer classification. The Taguchi method was used to determine the suitable number of neurons in a single hidden layer of the ANN. The selection of a suitable number of neurons helps to solve the overfitting problem by affecting the classification performance of an ANN. With this, a robust classification model was then built for breast cancer classification. Based on the Taguchi method results, the suitable number of neurons selected for the hidden layer in this study is 15, which was used for the training of the proposed ANN model. The developed model was benchmarked upon the Wisconsin Diagnostic Breast Cancer Dataset, popularly known as the UCI dataset. Finally, the proposed model was compared with seven other existing classification models, and it was confirmed that the model in this study had the best accuracy at breast cancer classification, at 98.8%. This confirmed that the proposed model significantly improved performance.

Breast Cancer Classification using Discrete Wavelet Transformation and Deep Learning

2019 ◽  
Vol 13 ◽  
Author(s):  
Emmanuel Masa-Ibi ◽  
Rajesh Prasad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Learning ◽  
Cancer Classification ◽  
Classification Model ◽  
Discrete Wavelet ◽  
Feed Forward Neural Network ◽  
Cancer Data ◽  
Breast Cancer Classification ◽  
The Neural Network

Background: One of the most prevalent sicknesses these days is breast cancer which is common amongst women. This sickness has been in increase to an alarming rate due to the lack of accurate administration of diagnoses. Early and accurate detection is one of the safest ways to cure a breast cancer patient. Objectives: The objective of this study is to proffer a more effective way to accurately classify a cancer sample; whether is Benign or Malignant. Methods: The classification model is based on the data collected from the UCI machine learning repository acquired from Wisconsin hospital called Wisconsin breast cancer data (WBCD). In this study, we preprocessed the dataset using DWT and then test the efficiency of deep learning (DL) for breast cancer classification. The model is developed using a feed-forward neural network and the result is compared with the observed values. Results: The result of the experiment proved the effectiveness of the proposed classification technique. The new technique accomplishes 98.90% accuracy for classifying breast cancer. Conclusions: The result from the experiment shows that the importance of data preprocessing and the efficiency of the neural network over other classification algorithms.

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.

scholarly journals Breast Cancer Classification Prediction Based on Ultrasonic Image Feature Recognition

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yihong Huang ◽  
Shuo Zheng ◽  
Yu Lin ◽  
Haiyan Miao
Keyword(s):  
Breast Cancer ◽  
Feature Recognition ◽  
Intelligent System ◽  
Cancer Classification ◽  
Image Feature ◽  
Classification Model ◽  
Ultrasound Probe ◽  
Ultrasonic Image ◽  
Breast Cancer Classification ◽  
Classification Prediction

Exploring an effective method to manage the complex breast cancer clinical information and selecting a suitable classifier for predictive modeling still require continuous research and verification in the actual clinical environment. This paper combines the ultrasound image feature algorithm to construct a breast cancer classification model. Furthermore, it combines the motion process of the ultrasound probe to accurately connect the ultrasound probe to the breast tumor. Moreover, this paper constructs a hardware and software system structure through machine vision algorithms and intelligent motion algorithms. Furthermore, it combines coordinate transformation and image recognition algorithms to expand the recognition process to realize automatic and intelligent real-time breast cancer diagnosis. In addition, this paper combines machine learning algorithms to process data and obtain an intelligent system model. Finally, this paper designs experiments to verify the intelligent system of this paper. Through experimental research, it can be seen that the breast cancer classification prediction system based on ultrasonic image feature recognition has certain effects.

scholarly journals Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection

2020 ◽  
Vol 20 (2) ◽  
pp. 712
Author(s):  
Nursabillilah Mohd Ali ◽  
Nor Azlina Ab Aziz ◽  
Rosli Besar
Keyword(s):  
Breast Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Diagnostic System ◽  
Cancer Classification ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Breast Cancer Patients ◽  
Cancer Dataset ◽  
Breast Cancer Classification

<p>Breast cancer is the most frequent cancer diagnosis amongst women worldwide. Despite the advancement of medical diagnostic and prognostic tools for early detection and treatment of breast cancer patients, research on development of better and more reliable tools is still actively conducted globally. The breast cancer classification is significantly important in ensuring reliable diagnostic system. Preliminary research on the usage of machine learning classifier and feature selection method for breast cancer classification is conducted here. Two feature selection methods namely Boruta and LASSO and SVM and LR classifier are studied. A breast cancer dataset from GEO web is adopted in this study. The findings show that LASSO with LR gives the best accuracy using this dataset.</p>

THE EFFECT OF DATASETS ON BREAST CANCER DETECTION MODELS

2021 ◽  
Vol 4 (4) ◽  
pp. 309-315
Author(s):  
Kumawuese Jennifer Kurugh ◽  
Muhammad Aminu Ahmad ◽  
Awwal Ahmad Babajo
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithms ◽  
Cancer Classification ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Machine Learning Classifiers ◽  
Breast Cancer Classification ◽  
Learning Classifiers

Datasets are a major requirement in the development of breast cancer classification/detection models using machine learning algorithms. These models can provide an effective, accurate and less expensive diagnosis method and reduce life losses. However, using the same machine learning algorithms on different datasets yields different results. This research developed several machine learning models for breast cancer classification/detection using Random forest, support vector machine, K Nearest Neighbors, Gaussian Naïve Bayes, Perceptron and Logistic regression. Three widely used test data sets were used; Wisconsin Breast Cancer (WBC) Original, Wisconsin Diagnostic Breast Cancer (WDBC) and Wisconsin Prognostic Breast Cancer (WPBC). The results show that datasets affect the performance of machine learning classifiers. Also, the machine learning classifiers have different performances with a given breast cancer dataset

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