Breast cancer diagnosis using an enhanced Extreme Learning Machine based-Neural Network

Nemissi, Mohamed; Salah, Halima; Séridi, Hamid

doi:10.1109/siva.2018.8661149

Cited by 23 publications

(5 citation statements)

References 20 publications

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“…Compared with the standard genetic algorithm optimized BP neural network algorithm, the recognition rate is increased by about 1.98%, and the accuracy of recognition is effectively improved. Compared with the k-Nearest Neighbor (KNN) proposed in [14], the recognition rate is improved by about 2.6%, and compared with the genetic algorithm optimized extreme learning machine neural network (GA-ELM-NN) proposed in [15], it is improved by about 0.22% The recognition rate shows that the algorithm proposed in this paper is superior to most other algorithms.…”

Section: Experiments and Analysismentioning

confidence: 89%

Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

Xie

Wang

2020

Communications in Computer and Information Science

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In recent years, the incidence of breast cancer is increasing and becomes one of the main causes of female death. The BP neural network optimized by standard genetic algorithm has slow convergence speed and is prone to local optimization, which makes the diagnosis accuracy of breast cancer decrease. This paper uses the improved genetic algorithm to optimize BP neural network by improving the selection operator of the standard genetic algorithm. The population diversity was first increased, and the probability of crossover and mutation was adaptively adjusted. Then deep optimization was executed on the initial weight threshold of BP network to speed up the network's convergence, and the number of iterations was reduced. Finally breast cancer diagnose was performed. The experiment results show that both the fitness of the improved genetic algorithm and the recognition accuracy of breast cancer are improved. The shortcomings of the standard genetic algorithm optimized BP neural network algorithm in breast cancer diagnosis are well solved.

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Section: Experiments and Analysismentioning

confidence: 89%

Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

Xie

Wang

2020

Communications in Computer and Information Science

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“…A study by NEMISSI et al [ 87 ] used an ELM with multiple activation functions for the hidden neurons and optimized them using a genetic algorithm. The proposed method improved generalization performance.…”

Section: Breast-cancer-diagnosis Methods Based On Deep Learningmentioning

confidence: 99%

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

Mridha

Hamid

Monowar

et al. 2021

Cancers

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Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.

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“…Nemissi et al [17] used an upgraded extreme learning machine (ELM) based-NN to BCD. They employed a neural classification method to diagnose BC in this study.…”

Section: Literature Reviewmentioning

confidence: 99%

A comparative study to predict breast cancer using machine learning techniques

Reddy¹,

Pilli²,

Voosala³

et al. 2022

IJEECS

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Detection of disease at the starting stage is a very crucial problem. As the population growth increases, the risk of death incurred by breast cancer rises exponentially. Breast cancer is the most common cancer in women, and it is also the most dangerous of all cancers. Deaths because of breast cancer have b een increasing in recent times. Earlier detection of the disease followed by treatment can reduce the risk and increase survival chances. There will be cases where even medical professionals can make mistakes in identifying the disease. This project deals with the detection of Breast cancer using the cell data of the tumor present in the breast. So, with the help of technologies in machine learning and artificial intelligence can substantially improve the diagnosis accuracy. The development of this project is beneficial in medical decision support systems. Several machine learning techniques, namely Adaboost, multi-layer perceptron (MLP) and stacking classifier; were used, and among all the algorithms, the stacking classifier results in the best accuracy. The accuracies 95.6%, 97.1%, and 99.2% respectively.

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Breast cancer diagnosis using an enhanced Extreme Learning Machine based-Neural Network

Cited by 23 publications

References 20 publications

Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

Breast Cancer Recognition Based on BP Neural Network Optimized by Improved Genetic Algorithm

A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

A comparative study to predict breast cancer using machine learning techniques

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