Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

Rasheed, Jawad; Shubair, Raed M.

doi:10.3390/healthcare10071313

Cited by 20 publications

(8 citation statements)

References 39 publications

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“…The results also indicate that it may be beneficial to train the algorithm further, possibly on a dataset acquired with a focus on including patients with small contrast-enhancing lesions. The techniques behind the algorithms transform well between different domains and radiographic modalities [21,35,36]. As a final remark, for training and evaluating segmentation algorithms, consensus guidelines for tumour delineation would be desirable.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

et al. 2023

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In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland–Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

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Section: Discussionmentioning

confidence: 99%

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

et al. 2023

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“…In addition to these, a researcher in [35] attained high accuracy of 99.93% by introducing a hybrid approach based on image filtering, feature-extraction, and SVM classifier to identify COVID-19, however, the study is conducted to solve a two-class classification task. Another three-class classification diagnostic system is proposed in [36]. They investigated various techniques and finally concluded that a hybrid combination of stationary wavelet transformation as data augmentation technique, AlexNet, ResNet101, and SqueezeNet as feature generators, iterative chi-square as feature selector, and SVM as classifier performed well to segregate COVID-19 cases from normal and other pneumonia cases.…”

Section: Summary and Discussionmentioning

confidence: 99%

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Rasheed¹,

Alsubai²

2023

Computers, Materials &Amp; Continua

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Humankind is facing another deadliest pandemic of all times in history, caused by COVID-19. Apart from this challenging pandemic, World Health Organization (WHO) considers tuberculosis (TB) as a preeminent infectious disease due to its high infection rate. Generally, both TB and COVID-19 severely affect the lungs, thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation. Therefore, the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases. As one of the preliminary smart health systems that examine three clinical states (COVID-19, TB, and normal cases), this study proposes an amalgam of image filtering, data-augmentation technique, transfer learning-based approach, and advanced deep-learning classifiers to effectively segregate these diseases. It first employed a generative adversarial network (GAN) and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise. Each pre-processed image is then converted into red, green, and blue (RGB) and Commission Internationale de l'Elcairage (CIE) color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50. Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network (RNN) classifiers for precise discrimination of threeclinical states. Comparative analysis showed that the proposed Bi-directional long-short-term-memory (Bi-LSTM) model dominated the long-short-termmemory (LSTM) network by attaining an overall accuracy of 98.22% for the three-class classification task, whereas LSTM hardly achieved 94.22% accuracy on the test dataset.

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“…The use of artificial intelligence (AI) in clinical care and public health contexts has expanded rapidly in recent years [1][2][3][4][5][6], including throughout the COVID-19 pandemic [7][8][9][10][11][12][13][14][15]. While emerging AI applications have the potential to improve health care quality and fairness [16][17][18][19][20][21], they may alternatively perpetuate or exacerbate inequities if they are not designed, deployed, and monitored appropriately [22][23][24][25][26].…”

Section: Background and Rationalementioning

confidence: 99%

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

Berdahl¹,

Baker²,

Mann³

et al. 2023

JMIR AI

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Background Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

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Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies

Cited by 20 publications

References 39 publications

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

Evaluation of the HD-GLIO Deep Learning Algorithm for Brain Tumour Segmentation on Postoperative MRI

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Strategies to Improve the Impact of Artificial Intelligence on Health Equity: Scoping Review

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