Review on chest pathogies detection systems using deep learning techniques

Rehman, Arshia; Khan, Ahmad; Fatima, Gohar; Naz, Saeeda; Razzak, Imran

doi:10.1007/s10462-023-10457-9

Cited by 4 publications

(1 citation statement)

References 188 publications

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“…Contingent to the type of HIS instance, health data output emerges from the health care applications. [53][54][55][56] These data frequently have been regarded with contentions around many data-associated issues such as the size, access and exchanges. 57,58 Azzopardi-Muscat, Kluge, Asma and Novillo-Ortiz 59 accentuated the need to strengthen data and information systems for unobstructed interchanges.…”

Section: Data Sourcesmentioning

confidence: 99%

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Epizitone,

Moyane,

Agbehadji

2023

JMDH

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Many transformations and uncertainties, such as the fourth industrial revolution and pandemics, have propelled healthcare acceptance and deployment of health information systems (HIS). External and internal determinants aligning with the global course influence their deployments. At the epic is digitalization, which generates endless data that has permeated healthcare. The continuous proliferation of complex and dynamic healthcare data is the digitalization frontier in healthcare that necessitates attention.Objective: This study explores the existing body of information on HIS for healthcare through the data lens to present a data-driven paradigm for healthcare augmentation paramount to attaining a sustainable and resilient HIS. Method: Preferred Reporting Items for Systematic Reviews and Meta-Analyses: PRISMA-compliant in-depth literature review was conducted systematically to synthesize and analyze the literature content to ascertain the value disposition of HIS data in healthcare delivery.Results: This study details the aspects of a data-driven paradigm for robust and sustainable HIS for health care applications. Data source, data action and decisions, data sciences techniques, serialization of data sciences techniques in the HIS, and data insight implementation and application are data-driven features expounded. These are essential data-driven paradigm building blocks that need iteration to succeed. Discussions: Existing literature considers insurgent data in healthcare challenging, disruptive, and potentially revolutionary. This view echoes the current healthcare quandary of good and bad data availability. Thus, data-driven insights are essential for building a resilient and sustainable HIS. People, technology, and tasks dominated prior HIS frameworks, with few data-centric facets. Improving healthcare and the HIS requires identifying and integrating crucial data elements. Conclusion:The paper presented a data-driven paradigm for a resilient and sustainable HIS. The findings show that data-driven track and components are essential to improve healthcare using data analytics insights. It provides an integrated footing for data analytics to support and effectively assist health care delivery.

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Section: Data Sourcesmentioning

confidence: 99%

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Epizitone,

Moyane,

Agbehadji

2023

JMDH

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Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection

Yuan,

Liu,

Yang

et al. 2024

J Digit Imaging. Inform. med.

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Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes

Bougourzi,

Dornaika,

Nakib

et al. 2024

Artif Intell Rev

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One of the primary challenges in applying deep learning approaches to medical imaging is the limited availability of data due to various factors. These factors include concerns about data privacy and the requirement for expert radiologists to perform the time-consuming and labor-intensive task of labeling data, particularly for tasks such as segmentation. Consequently, there is a critical need to develop novel approaches for few-shot learning tasks in this domain. In this work, we propose a Novel CNN-Transformer Fusion scheme to segment Multi-classes pneumonia infection from limited CT-scans data. In total, there are three main contributions: (i) CNN-Transformer encoders fusion, which allows to extract and fuse richer features in the encoding phase, which contains: local, global and long-range dependencies features, (ii) Multi-Branches Skip Connection (MBSC) is proposed to extract and fuse richer features from the encoder features then integrate them into the decoder layers, where MBSC blocks extract higher-level features related to the finer details of different infection types, and (iii) a Multi-classes Boundary Aware Cross-Entropy (MBA-CE) Loss function is proposed to deal with fuzzy boundaries, enhance the separability between classes and give more attention to the minority classes. The performance of the proposed approach is evaluated using two evaluation scenarios and compared with different baseline and state-of-the-art segmentation architectures for Multi-classes Covid-19 segmentation. The obtained results show that our approach outperforms the comparison methods in both Ground-Glass Opacity (GGO) and Consolidation segmentation. On the other hand, our approach shows consistent performance when the training data is reduced to half, which proves the efficiency of our approach in few-shot learning. In contrast, the performance of the comparison methods drops in this scenario. Moreover, our approach is able to deal with imbalanced data classes. These advantages prove the effectiveness and efficiency of the proposed EMB-TrAttUnet approach in a pandemic scenario where time is critical to save patient lives.

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Review on chest pathogies detection systems using deep learning techniques

Cited by 4 publications

References 188 publications

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

A Data-Driven Paradigm for a Resilient and Sustainable Integrated Health Information Systems for Health Care Applications

Multi-scale Lesion Feature Fusion and Location-Aware for Chest Multi-disease Detection

Emb-trattunet: a novel edge loss function and transformer-CNN architecture for multi-classes pneumonia infection segmentation in low annotation regimes

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