Convolutional neural network improvement for breast cancer classification

Ting, Fung Fung; Tan, Y. J.; Sim, K. S.

doi:10.1016/j.eswa.2018.11.008

Cited by 349 publications

(155 citation statements)

References 26 publications

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“…Each hidden layer of neural network will be used to classify a specific part of the input data. Therefore using a single hidden layer pattern recognition system will reduce the accuracy of CNN [1].…”

Section: Conventional Neural Network (Cnn) Recursive Neural Network mentioning

confidence: 99%

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

Priyadharshini,

Zoraida

2019

IJEAT

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Cancer detecting technology plays a vital role in the medical community. Researches have shown that patients that are affected by cancer carry same type of genetic patterns in their DNA. With this in mind, this research work concentrates on analysing gene pattern for detecting cancer using deep learning algorithms. The Feedback based Adaptive Recurrent Neural Network (FA-RNN) approach is designed to classify and analyse the gene pattern recognition. The data augmentation is done to improve the quality of the input data from COSMIC dataset which includes the detection of missing values, removing the noise present in input using multiple imputations and reducing higher base value can be done using dimensionality reduction process. After obtaining the improved dataset, the training phase begins by estimating the exact weight value of feedback layer using feedback weight loop calculation technique to lessen number of repetition during training. Moreover, the error calculation is done to evaluate the exact weight values of feedback layer used for classification. Finally the classification is done by selecting the next appropriate hidden neuron using the neuron selection activation function. The performance of the Feedback based Adaptive Recurrent Neural Network technique can be analysed using the evaluation metrics accuracy, computation time and Root Mean Square Error (RMSE) and the attained results are compared with the Recursive Neural Network(RNN) and Convolutional Neural Network(CNN) algorithms. The obtained results such as higher accuracy, reduced RMSE and less computation time in Feedback based Adaptive Recurrent Neural Network indicates that it performs the enhanced operation than CNN and RNN.

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Section: Conventional Neural Network (Cnn) Recursive Neural Network mentioning

confidence: 99%

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

Priyadharshini,

Zoraida

2019

IJEAT

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“…It can be helpful for treatment planning, diagnosis, screening, therapy evaluation, modeling of pathology, etc. 1,2,12,64 Although several interesting solutions have been proposed to solve some related biomedical classification issues, however, the main obstacles facing researchers to conduct a careful analysis of some biomedical applications are basically the following: (a) the low of contrast in medical imaging, (b) the diversity of shapes and appearance for tumors, and (c) the similarity in properties between normal and abnormal tissue in many cases. On the other hand, performing manually such tasks like edge detection is generally timeconsuming process and impractical.…”

Section: Introductionmentioning

confidence: 99%

“…Several computer-aided diagnosis (CAD) systems and classification techniques were developed to address the problem of mammographic lesions detection including, for example, artificial intelligence, deep learning, convolutional neural network (CNN), and support vector machines (SVM). 1,2,12,64 Although several interesting solutions have been proposed to solve some related biomedical classification issues, however, the main obstacles facing researchers to conduct a careful analysis of some biomedical applications are basically the following: (a) the low of contrast in medical imaging, (b) the diversity of shapes and appearance for tumors, and (c) the similarity in properties between normal and abnormal tissue in many cases. All these causes do not allow correct decisions taken by specialists on such images and can cause a useless biopsy.…”

Section: Introductionmentioning

confidence: 99%

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

Alroobaea

Rubaiee

Bourouis

et al. 2019

Int J Imaging Syst Tech

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Biomedical image classification problem has attracted a lot of attention in medical engineering community and medicine applications. Accurate and automatic classification (eg, normal/abnormal or malignant/benign) has a variety of applications such as automatic decision making and is known to be very challenging. In this research, we address this problem by investigating the effectiveness of Bayesian inference methods for statistical bounded mixture models. Indeed, a novel approach termed as Bayesian learning for bounded generalized Gaussian mixture models is developed. The consideration of bounded mixture models is encouraged by their capability to take into account the nature of the data that is compactly supported. Furthermore, the consideration of Bayesian inference is more attractive compared to frequentist reasoning. In this work, we address main issues related to accurate data classification such as the effective estimation of the model's parameters and the selection of the optimal model complexity. Moreover, the problem of over-or under-fitting is treated by taking into account the uncertainty through introducing prior information about the model's parameters. A comparative study between different Gaussian-based models is also performed to evaluate the performance of the proposed framework. Experiments have been conducted on challenging biomedical image datasets that involve retinal images for diabetic retinopathy detection and mammograms for breast cancer detection. Obtained results are encouraging and show the benefits of our Bayesian framework. K E Y W O R D SBayesian inference, biomedical imaging, bounded mixture models, generalized Gaussian distribution, image classification, Markov chain Monte Carlo (MCMC)

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“…Machine learning plays a unique and important role in the field of cancer treatment. For example, some researchers applied neural networks to the classification of breast cancer [2,3], and Dongmei Ai et al [4] identified intestinal microorganisms associated with colorectal cancer by means of decision tree aggregation with a random forest model. Support vector machine (SVM) is a supervised machine learning method used to solve classification and regression problems, firstly proposed by Vapnik on the basis of statistical learning theory [5].…”

Section: Introductionmentioning

confidence: 99%

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

2019

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As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.

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Convolutional neural network improvement for breast cancer classification

Cited by 349 publications

References 26 publications

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

Feedback Based Adaptive Recurrent Neural Network for Cancer Detection using Gene Data Pattern

Bayesian inference framework for bounded generalized Gaussian‐based mixture model and its application to biomedical images classification

DA-Based Parameter Optimization of Combined Kernel Support Vector Machine for Cancer Diagnosis

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