Breast Cancer Classification Prediction Based on Ultrasonic Image Feature Recognition

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.

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Intelligent system based on local linear wavelet neural network and recursive least square approach for breast cancer classification

Artificial Intelligence Review ◽

10.1007/s10462-011-9263-5 ◽

2011 ◽

Vol 39 (2) ◽

pp. 151-163 ◽

Cited By ~ 9

Author(s):

M. R. Senapati ◽

P. K. Dash

Keyword(s):

Breast Cancer ◽

Neural Network ◽

Intelligent System ◽

Wavelet Neural Network ◽

Cancer Classification ◽

Least Square ◽

Recursive Least Square ◽

Breast Cancer Classification ◽

Local Linear ◽

Least Square Approach

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Breast Cancer Classification using Discrete Wavelet Transformation and Deep Learning

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813666191224104500 ◽

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.

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Classification of Breast Cancer Using Neutrosophic Techniques and Deep Neural Network

10.21203/rs.3.rs-771965/v1 ◽

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

PeerJ Computer Science ◽

10.7717/peerj-cs.344 ◽

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.

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