Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network

Zemouri, Ryad; Omri, Nabil; Devalland, Christine; Arnould́, Laurent; Morello, Brigitte; Zerhouni, Noureddine; Fnaiech, Farhat

doi:10.1109/mecbme.2018.8402426

Cited by 20 publications

(13 citation statements)

References 42 publications

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“…Early determination of this cancer increases survival chances, but women residing in medically underserved areas do not have access to specialist doctors. Machine learning and cloud computing services have drawn the attention of various researchers for developing disease prediction systems, such as [70][71][72][73][74][75][76][77][78], as a feasible option in remote diagnostics, where cloud computing provided Platform-as-a-Service (PaaS) to obtain resources on demand.…”

Section: Performance Comparison Of Elm On the Cloud Environment And Standalone Environmentmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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Globally, breast cancer is one of the most significant causes of death among women. Early detection accompanied by prompt treatment can reduce the risk of death due to breast cancer. Currently, machine learning in cloud computing plays a pivotal role in disease diagnosis, but predominantly among the people living in remote areas where medical facilities are scarce. Diagnosis systems based on machine learning act as secondary readers and assist radiologists in the proper diagnosis of diseases, whereas cloud-based systems can support telehealth services and remote diagnostics. Techniques based on artificial neural networks (ANN) have attracted many researchers to explore their capability for disease diagnosis. Extreme learning machine (ELM) is one of the variants of ANN that has a huge potential for solving various classification problems. The framework proposed in this paper amalgamates three research domains: Firstly, ELM is applied for the diagnosis of breast cancer. Secondly, to eliminate insignificant features, the gain ratio feature selection method is employed. Lastly, a cloud computing-based system for remote diagnosis of breast cancer using ELM is proposed. The performance of the cloud-based ELM is compared with some state-of-the-art technologies for disease diagnosis. The results achieved on the Wisconsin Diagnostic Breast Cancer (WBCD) dataset indicate that the cloud-based ELM technique outperforms other results. The best performance results of ELM were found for both the standalone and cloud environments, which were compared. The important findings of the experimental results indicate that the accuracy achieved is 0.9868, the recall is 0.9130, the precision is 0.9054, and the F1-score is 0.8129.

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Section: Performance Comparison Of Elm On the Cloud Environment And Standalone Environmentmentioning

confidence: 99%

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

et al. 2021

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“…Baltres et al [ 32 ] proposed a deep learning-based approach to predict an alternative recurrent score of Oncotype DX using histopathological characteristics. Zemouri et al [ 33 ] proposed a new joint variable selection and constructive deep neural network “ConstDeepNet”-based approach. The authors applied the variable selection method to improve the training of the deep learning model.…”

Section: Related Workmentioning

confidence: 99%

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation

et al. 2021

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Breast cancer is a serious threat to women. Many machine learning-based computer-aided diagnosis (CAD) methods have been proposed for the early diagnosis of breast cancer based on histopathological images. Even though many such classification methods achieved high accuracy, many of them lack the explanation of the classification process. In this paper, we compare the performance of conventional machine learning (CML) against deep learning (DL)-based methods. We also provide a visual interpretation for the task of classifying breast cancer in histopathological images. For CML-based methods, we extract a set of handcrafted features using three feature extractors and fuse them to get image representation that would act as an input to train five classical classifiers. For DL-based methods, we adopt the transfer learning approach to the well-known VGG-19 deep learning architecture, where its pre-trained version on the large scale ImageNet, is block-wise fine-tuned on histopathological images. The evaluation of the proposed methods is carried out on the publicly available BreaKHis dataset for the magnification dependent classification of benign and malignant breast cancer and their eight sub-classes, and a further validation on KIMIA Path960, a magnification-free histopathological dataset with 20 image classes, is also performed. After providing the classification results of CML and DL methods, and to better explain the difference in the classification performance, we visualize the learned features. For the DL-based method, we intuitively visualize the areas of interest of the best fine-tuned deep neural networks using attention maps to explain the decision-making process and improve the clinical interpretability of the proposed models. The visual explanation can inherently improve the pathologist’s trust in automated DL methods as a credible and trustworthy support tool for breast cancer diagnosis. The achieved results show that DL methods outperform CML approaches where we reached an accuracy between 94.05% and 98.13% for the binary classification and between 76.77% and 88.95% for the eight-class classification, while for DL approaches, the accuracies range from 85.65% to 89.32% for the binary classification and from 63.55% to 69.69% for the eight-class classification.

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“…According to Reference [2], among the first techniques, we can find k-Nearest Neighbor—k-NN, Artificial Neural Network—ANN, Decision Tree—DT, Support Vector Machine—SVM, random forest, among others. Within the second one, we can find Convolutional Neural Networks (CNN) [6,7,8], Constructive Deep Neural Network (CDNN) [9], Deep Neural Network (DNN) [10], a Deep Belief Network (DBN) [11], a Deep Boltzmann Machine (DBM) [12] as well as others. For the third category, according to Reference [4], we can find Fuzzy Expert Systems (FES), the Fuzzy Set Theory (FST), the Fuzzy Inference Systems (FIS), the Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Fuzzy Neural Networks (FNN), fuzzy cognitive maps, and more.…”

Section: Introductionmentioning

confidence: 99%

“…For Clinical Decision Support Systems, many predictive or classification algorithms have been implemented to diagnose different diseases [15]. Among them, we can find works using fuzzy rule miner [16], Constructive Deep Neural Network [9], Support Vector Machines [17,18], ANFIS [19,20], genetic algorithms [21], random forest [22,23], Decision Trees [24,25], k-NN [24], and more. Most of these intelligent systems have their algorithms for “learning” from the data.…”

Section: Introductionmentioning

confidence: 99%

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network

Cited by 20 publications

References 42 publications

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Cloud Computing-Based Framework for Breast Cancer Diagnosis Using Extreme Learning Machine

Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

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