Constructive Deep Neural Network for Breast Cancer Diagnosis

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

doi:10.1016/j.ifacol.2018.11.660

Cited by 20 publications

(9 citation statements)

References 32 publications

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“…A radiomics model based on three-dimensional segmentation should be developed in future studies. Finally, more and more studies on BC diagnosis were performed based on deep neural learning, which was a subset of machine learning and unsupervised from data that were unstructured and unlabeled [40][41][42][43]. In our study, only conventional radiomics analysis was investigated, and the difference of performance and robustness in evaluating HER-2 2+ status should be further compared between our study and those based on deep neural network.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

Yin

2021

Diagnostics

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This study aimed to establish and validate a radiomics nomogram using the radiomics score (rad-score) based on multiregional diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) features combined with clinical factors for evaluating HER-2 2+ status of breast cancer. A total of 223 patients were retrospectively included. Radiomic features were extracted from multiregional DWI and ADC images. Based on the intratumoral, peritumoral, and combined regions, three rad-scores were calculated using the logistic regression model. Independent parameters were selected among clinical factors and combined rad-score (com-rad-score) using multivariate logistic analysis and used to construct a radiomics nomogram. The performance of the nomogram was evaluated using calibration, discrimination, and clinical usefulness. The areas under the receiver operator characteristic curve (AUCs) of intratumoral and peritumoral rad-scores were 0.824/0.763 and 0.794/0.731 in the training and validation cohorts, respectively. Com-rad-score achieved the highest AUC (0.860/0.790) among three rad-scores. ER status and com-rad-score were selected to establish the nomogram, which yielded good discrimination (AUC: 0.883/0.848) and calibration. Decision curve analysis demonstrated the clinical value of the nomogram in the validation cohort. In conclusion, radiomics nomogram, including clinical factors and com-rad-score, showed favorable performance for evaluating HER-2 2+ status in breast cancer.

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Section: Discussionmentioning

confidence: 99%

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

Yin

2021

Diagnostics

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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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“…By contrast, biomedical datasets tend to be heterogenous, difficult to annotate, and relatively scarce [10,11]. In two recent breast imaging studies that used artificial intelligence (AI), the dataset sizes for breast lesion detection and breast cancer recurrence were 320 and 92 patients, respectively [12,13]. Medical studies often lack a combination of publicly available data and high-quality labels [1,14].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

et al. 2020

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(1) Background: The effectiveness of deep learning artificial intelligence depends on data availability, often requiring large volumes of data to effectively train an algorithm. However, few studies have explored the minimum number of images needed for optimal algorithmic performance. (2) Methods: This institutional review board (IRB)-approved retrospective review included patients who received prostate magnetic resonance imaging (MRI) between September 2014 and August 2018 and a magnetic resonance imaging (MRI) fusion transrectal biopsy. T2-weighted images were manually segmented by a board-certified abdominal radiologist. Segmented images were trained on a deep learning network with the following case numbers: 8, 16, 24, 32, 40, 80, 120, 160, 200, 240, 280, and 320. (3) Results: Our deep learning network’s performance was assessed with a Dice score, which measures overlap between the radiologist’s segmentations and deep learning-generated segmentations and ranges from 0 (no overlap) to 1 (perfect overlap). Our algorithm’s Dice score started at 0.424 with 8 cases and improved to 0.858 with 160 cases. After 160 cases, the Dice increased to 0.867 with 320 cases. (4) Conclusions: Our deep learning network for prostate segmentation produced the highest overall Dice score with 320 training cases. Performance improved notably from training sizes of 8 to 120, then plateaued with minimal improvement at training case size above 160. Other studies utilizing comparable network architectures may have similar plateaus, suggesting suitable results may be obtainable with small datasets.

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Constructive Deep Neural Network for Breast Cancer Diagnosis

Cited by 20 publications

References 32 publications

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer

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

Deep Learning with Limited Data: Organ Segmentation Performance by U-Net

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