Classification techniques in breast cancer diagnosis: A systematic literature review

ElOuassif, Bouchra; Idri, Ali; Hosni, Mohamed; Abran, Alain

doi:10.1080/21681163.2020.1811159

Cited by 17 publications

(8 citation statements)

References 172 publications

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“…Current research tools are divided into nonparametric and parametric approaches: the nonparametric approach does not require the assumption of knowledge of any parameters and uses nonparametric techniques to estimate the density of the distribution, for example, histograms and Parzen window estimation; the parametric approach requires the assumption that normal data are generated based on parametric distributions, and it requires these parameters from training samples, for example, outlier detection methods based on normal distributions [13]. Neural networks, which may be classified into single-classification neural networks and multiclassification neural networks [14], are an important field of nonlinear modeling approaches. Multiclassification neural networks, such as multilayer perceptron, neural tree, and others, use data from multiple classifications to train the model and then input test data into the model, which the network interprets as normal or abnormal; single-classification neural networks, such as Replicator Neural Networks (RNNs), use a function (like a step function) to transform the sample into N discrete variables for sample clustering.…”

Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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In response to the shortcomings of the traditional methods for evaluating the mental health status of college students in terms of computational complexity and low accuracy, a method for evaluating the mental health status of college students based on data reduction and support vector machines was proposed. A model experiment containing internal and external personality tendency classification, anxiety, and depression dichotomy was designed using logistic regression analysis, information entropy, and SVM algorithm to construct the feature dimensions of the network behavior data, combined with the labeled data of mental state to derive the sample data set for model experiments. In the experimental process, to reflect the difference in the effect of different models, various types of mathematical models were constructed for horizontal comparison; at the same time, to reflect the influence of the parameters of the same type of model, different combinations of parameters were constructed using a grid search algorithm to vertically compare the difference in the effect. The average accuracy of the dichotomous model for anxiety and depression in the sample of 1433 students was 0.80 or higher. The experiments show that the method of predicting students’ psychological status through their online behavioral data is feasible, and the mathematical classification model can be used to grasp students’ psychological status in real time and to warn students with abnormal psychological status, thus helping school counselors to intervene and prevent them promptly.

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Section: Current Status Of Researchmentioning

confidence: 99%

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Peiqing

2022

Journal of Mathematics

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“…Khuriwal et al [16] used a DL model for breast cancer diagnosis. Their method includes three basic steps, such as (1) pre-processing (data collection and filtration), (2) splitting the data into train and test sets and drawing a graph for visualization. Finally, a model is developed, and data is fed into it.…”

Section: Literature Reviewmentioning

confidence: 99%

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

Thwin,

Malebary,

Abulfaraj

et al. 2024

Technologies

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Globally, breast cancer (BC) is considered a major cause of death among women. Therefore, researchers have used various machine and deep learning-based methods for its early and accurate detection using X-ray, MRI, and mammography image modalities. However, the machine learning model requires domain experts to select an optimal feature, obtains a limited accuracy, and has a high false positive rate due to handcrafting features extraction. The deep learning model overcomes these limitations, but these models require large amounts of training data and computation resources, and further improvement in the model performance is needed. To do this, we employ a novel framework called the Ensemble-based Channel and Spatial Attention Network (ECS-A-Net) to automatically classify infected regions within BC images. The proposed framework consists of two phases: in the first phase, we apply different augmentation techniques to enhance the size of the input data, while the second phase includes an ensemble technique that parallelly leverages modified SE-ResNet50 and InceptionV3 as a backbone for feature extraction, followed by Channel Attention (CA) and Spatial Attention (SA) modules in a series manner for more dominant feature selection. To further validate the ECS-A-Net, we conducted extensive experiments between several competitive state-of-the-art (SOTA) techniques over two benchmarks, including DDSM and MIAS, where the proposed model achieved 96.50% accuracy for the DDSM and 95.33% accuracy for the MIAS datasets. Additionally, the experimental results demonstrated that our network achieved a better performance using various evaluation indicators, including accuracy, sensitivity, and specificity among other methods.

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“… ElOuassif et al (2021) has proposed many neural networks and example methods for the classification of breast cancer. Nassif et al (2022) had focused on convolutional neural networks.…”

Section: Related Workmentioning

confidence: 99%

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

Jakkaladiki¹,

Malý²

2023

PeerJ Computer Science

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Breast cancer has been the most life-threatening disease in women in the last few decades. The high mortality rate among women is due to breast cancer because of less awareness and a minimum number of medical facilities to detect the disease in the early stages. In the recent era, the situation has changed with the help of many technological advancements and medical equipment to observe breast cancer development. The machine learning technique supports vector machines (SVM), logistic regression, and random forests have been used to analyze the images of cancer cells on different data sets. Although the particular technique has performed better on the smaller data set, accuracy still needs to catch up in most of the data, which needs to be fairer to apply in the real-time medical environment. In the proposed research, state-of-the-art deep learning techniques, such as transfer learning, based cross model classification (TLBCM), convolution neural network (CNN) and transfer learning, residual network (ResNet), and Densenet proposed for efficient prediction of breast cancer with the minimized error rating. The convolution neural network and transfer learning are the most prominent techniques for predicting the main features in the data set. The sensitive data is protected using a cyber-physical system (CPS) while using the images virtually over the network. CPS act as a virtual connection between human and networks. While the data is transferred in the network, it must monitor using CPS. The ResNet changes the data on many layers without compromising the minimum error rate. The DenseNet conciliates the problem of vanishing gradient issues. The experiment is carried out on the data sets Breast Cancer Wisconsin (Diagnostic) and Breast Cancer Histopathological Dataset (BreakHis). The convolution neural network and the transfer learning have achieved a validation accuracy of 98.3%. The results of these proposed methods show the highest classification rate between the benign and the malignant data. The proposed method improves the efficiency and speed of classification, which is more convenient for discovering breast cancer in earlier stages than the previously proposed methodologies.

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Classification techniques in breast cancer diagnosis: A systematic literature review

Cited by 17 publications

References 172 publications

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

Attention-Based Ensemble Network for Effective Breast Cancer Classification over Benchmarks

An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer

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