Cross-task extreme learning machine for breast cancer image classification with deep convolutional features

Pin, Wang; Qi, Song; Li, Yongming; Lv, Shanshan; Wang, Jiaxin; Li, Linyu; Zhang, HeHua

doi:10.1016/j.bspc.2019.101789

Cited by 48 publications

(21 citation statements)

References 21 publications

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“…The error function E is defined as the distance between the input vector and the connection weight vector. E is calculated by (4),…”

Section: A Extract Multiple Correlation Indicators Based On Som Clusmentioning

confidence: 99%

“…Different algorithms have been explored to analyze the influencing factors of cancer and predict the risk level of cancer. Different statistical and machine learning techniques have been used to develop cancer prediction models, including random forest [3], extreme learning machine [4], naive bayes [5], artificial neural networks [6], and support vector machine [7].…”

Section: Introductionmentioning

confidence: 99%

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Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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This article provides a method based on self-organizing maps (SOM) neural network clustering and support vector machine (SVM) ensembles to predict the survival risk levels of esophageal cancer. Nine blood indexes related to patient survival are found by using SOM clustering method. Two critical thresholds for survival are found by plotting the receiver operating characteristic (ROC) curve twice, and the lifetime is divided into three risk levels. Using the SVM method, patients' risk levels are predicted and assessed. Four kernel functions of SVM are compared, and the prediction effect of RBF kernel function is better than other kernel functions. The parameters of SVM are optimized by using genetic algorithm (GA), particle swarm algorithm (PSO) and artificial bee colony (ABC) algorithm. Experimental results show that the prediction accuracies are improved by using optimization algorithms. After comparison, ABC-SVM has better prediction results than GA-SVM and PSO-SVM with a high prediction rate and fast running time. INDEX TERMS artificial bee colony, genetic algorithm, particle swarm optimization, self-organizing maps, support vector machine.

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“…The error function E is defined as the distance between the input vector and the connection weight vector. E is calculated by (4),…”

Section: A Extract Multiple Correlation Indicators Based On Som Clusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

et al. 2020

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“…Extreme Learning Machine ( ELM ) is one typical network training algorithm, which initializes the network weights randomly and then update the weight matrix in the output layer based on a least-square model (Wang et al, 2020 ). Experiments have shown the advantage of ELM to have easy implementation and better generalization ability, compared to the traditional backpropagation training algorithm.…”

Section: Resultsmentioning

confidence: 99%

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

Yang

Liu

et al. 2020

Front. Comput. Neurosci.

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The problem of cancer risk analysis is of great importance to health-service providers and medical researchers. In this study, we propose a novel Artificial Neural Network (ANN) algorithm based on the probabilistic framework, which aims to investigate patient patterns associated with their disease development. Compared to the traditional ANN where input features are directly extracted from raw data, the proposed probabilistic ANN manipulates original inputs according to their probability distribution. More precisely, the Naïve Bayes and Markov chain models are used to approximate the posterior distribution of the raw inputs, which provides a useful estimation of subsequent disease development. Later, this distribution information is further leveraged as additional input to train ANN. Additionally, to reduce the training cost and to boost the generalization capability, a sparse training strategy is also introduced. Experimentally, one of the largest cancer-related datasets is employed in this study. Compared to state-of-the-art methods, the proposed algorithm achieves a much better outcome, in terms of the prediction accuracy of subsequent disease development. The result also reveals the potential impact of patients' disease sequence on their future risk management.

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“…The generalization performance of CNN can be enhanced by double-step transfer learning for feature extraction. This model was developed in [13], where the classification is performed using interactive cross-task extreme learning machine (ICELM). The proposed model reported an average accuracy rate of 98.18% using the BreaKHis database.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

Elmannai¹,

Hamdi²,

Algarni³

2021

IJCIS

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Breast cancer is one of the foremost reasons of death among women in the world. It has the largest mortality rate compared to the types of cancer accounting for 1.9 million per year in 2020. An early diagnosis may increase the survival rates. To this end, automating the analysis and the diagnosis allows to improve the accuracy and to reduce processing time. However, analyzing breast imagery's is non-trivial and may lead to experts' disagreements. In this research, we focus on breast cancer histopathological images acquired using the microscopic scan of breast tissues. We present combined two deep convolutional neural networks (DCNNs) to extract distinguished image features using transfer learning. The pre-trained Inception and the Xceptions models are used in parallel. Then, the feature maps are combined and reduced by dropout before being fed to the last fully connected layers for classification. We follow a sub-image classification then a whole image classification based on majority vote and maximum probability rules. Four tissue malignancy levels are considered: normal, benign, in situ carcinoma, and invasive carcinoma. The experimentations are performed to the Breast Cancer Histology (BACH) dataset. The overall accuracy for the sub-image classification is 97.29% and for the carcinoma cases the sensitivity achieved 99.58%. The whole image classification overall accuracy reaches 100% by majority vote and 95% by maximum probability fusion decision. The numerical results showed that our proposed approach outperforms the previous methods in terms of accuracy and sensitivity. The proposed design allows an extension to whole-slide histology images classification.

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Cross-task extreme learning machine for breast cancer image classification with deep convolutional features

Cited by 48 publications

References 21 publications

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Survival Risk Prediction of Esophageal Cancer Based on Self-Organizing Maps Clustering and Support Vector Machine Ensembles

Cancer Risk Analysis Based on Improved Probabilistic Neural Network

Deep Learning Models Combining for Breast Cancer Histopathology Image Classification

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