Classification of oesophagic early-stage cancers: deep learning versus traditional learning approaches

Ferreira, Jorge; Sousa, Olga; Sampaio, Inês Lucena; Domingues, Inês

doi:10.1109/bibe50027.2020.00127

Cited by 6 publications

(8 citation statements)

References 18 publications

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“…Therefore, this study aimed to conduct a systematic review of the literature to investigate the use of ML in the early detection of EC. The study sought to address the research questions by synthesizing the ndings from previous studies in the eld (15,19).…”

Section: Discussionmentioning

confidence: 99%

“…In the eld of early detection of EC, different imaging modalities such as gastroscopy, WLI, and narrow-band imaging (NBI) have been used in various studies (19)(20)(21). A review of the literature showed that WLI images were used in 35% of studies (11,17,(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33), followed by a combination of WLI and NBI images in 10% (18, 23,34,35), CT images in 13% (36-39), NBI images in 3% (40), images of other modalities in 13% (41)(42)(43)(44), and the type of imaging was not mentioned in 26% of studies (Fig.…”

Section: Ec Image Segmentationmentioning

confidence: 99%

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Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

Hosseini

Asadi

Emami

et al. 2023

Preprint

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Introduction: Esophageal cancer (EC) is a significant global health problem, with an estimated 7th highest incidence and 6th highest mortality rate. Timely diagnosis and treatment are critical for improving patients' outcomes, as over 40% of patients with EC are diagnosed after metastasis. Recent advances in machine learning (ML) techniques, particularly in computer vision, have demonstrated promising applications in medical image processing, assisting clinicians in making more accurate and faster diagnostic decisions. Given the significance of early detection of EC, this systematic review aims to summarize and discuss the current state of research on ML-based methods for the early detection of EC. Methods: We conducted a comprehensive systematic search of five databases (PubMed, Scopus, Web of Science, Wiley, and IEEE) using search terms such as "ML", "Deep Learning (DL(", "Neural Networks (NN)", "Esophagus”, “EC" and “Early Detection”. After applying inclusion and exclusion criteria, 31 articles were retained for full review. Results: The results of this review highlight the potential of ML-based methods in the early detection of EC. The average accuracy of the reviewed methods in the analysis of endoscopic and computed tomography (CT (images of the esophagus was over 89%, indicating a high impact on early detection of EC. Additionally, the highest percentage of clinical images used in the early detection of EC with the use of ML was related to white light imaging (WLI) images. Among all ML techniques, methods based on convolutional neural networks (CNN) achieved higher accuracy and sensitivity in the early detection of EC compared to other methods. Conclusion: Our findings suggest that ML methods may improve accuracy in the early detection of EC, potentially supporting radiologists, endoscopists, and pathologists in diagnosis and treatment planning. However, the current literature is limited, and more studies are needed to investigate the clinical applications of these methods in early detection of EC. Furthermore, many studies suffer from class imbalance and biases, highlighting the need for validation of detection algorithms across organizations in longitudinal studies.

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Section: Discussionmentioning

confidence: 99%

Section: Ec Image Segmentationmentioning

confidence: 99%

Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

Hosseini

Asadi

Emami

et al. 2023

Preprint

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“…Results of manually extracting the features in the traditional machine learning method and convolutional deep learning methods, which extract their own features, were compared. When traditional and deep learning techniques were used in unison 100% accuracy was achieved; whereas, an accuracy slightly less than 93% was obtained using only traditional machine learning methods [35].…”

Section: Related Workmentioning

confidence: 91%

Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach

et al. 2021

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The World Health Organization estimates that approximately 10 million people are newly diagnosed with dementia each year and a global prevalence of nearly 50 million persons with dementia (PwD). The vast majority of PwD living at home receive the majority of their care from informal familial caregivers. The quality of life (QOL) of familial caregivers may be significantly impacted by their caregiving responsibilities and resultant caregiver burden. A major contributor to caregiver burden is the random occurrence of agitation in PwD and familial caregivers' lack of preparedness to manage these episodes. Caregiver burden may be reduced if it is possible to forecast impending agitation episodes. In this study, we leverage datadriven deep learning models to predict agitation episodes in PwD. We used Long Short-Term Memory (LSTM), a deep learning class of algorithms, to forecast agitations up to 30 min before actual agitation events. In particular, we managed the missing data by estimating the missing values and compensated for the class imbalance challenge by down-sampling the majority class. The simulations were based on real-world data from Alzheimer's disease (AD) caregivers and PwD dyads home environments, including ambient noise level, illumination, room temperature, atmospheric pressure (Pa), and relative humidity. Our results show the efficacy of data-driven deep learning models in predicting agitation episodes in communitydwelling AD dyads with accuracy of 98.6% and recall (sensitivity) of 84.8%.

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“…Studies of EC ( 29 ), CRC ( 52 ), and BC ( 53 ) have used deep learning-based methods for T-staging. This type studies are relatively lacking, since doctors usually use optical endoscopy to screen polyps in hollow organs.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

“…For deep learning-based methods, since there is no need to segment the region of interest (ROI), the segmentation of tumor and organ wall is not necessary ( 29 , 30 ). However, end-to-end structures are often disfavored for their inexplicability.…”

Section: The Pipeline Of Radiomics For T-stagingmentioning

confidence: 99%

Radiomics-based T-staging of hollow organ cancers

Huang,

Xu,

et al. 2023

Front. Oncol.

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Cancer growing in hollow organs has become a serious threat to human health. The accurate T-staging of hollow organ cancers is a major concern in the clinic. With the rapid development of medical imaging technologies, radiomics has become a reliable tool of T-staging. Due to similar growth characteristics of hollow organ cancers, radiomics studies of these cancers can be used as a common reference. In radiomics, feature-based and deep learning-based methods are two critical research focuses. Therefore, we review feature-based and deep learning-based T-staging methods in this paper. In conclusion, existing radiomics studies may underestimate the hollow organ wall during segmentation and the depth of invasion in staging. It is expected that this survey could provide promising directions for following research in this realm.

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Classification of oesophagic early-stage cancers: deep learning versus traditional learning approaches

Cited by 6 publications

References 18 publications

Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

Machine Learning Applications for Early Detection of Esophageal Cancer: A Systematic Review

Data-Driven Forecasting of Agitation for Persons with Dementia: A Deep Learning-Based Approach

Radiomics-based T-staging of hollow organ cancers

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