Towards Esophagitis and Barret’s Esophagus Endoscopic Images Classification: An Approach with Deep Learning Techniques

Villagrana-Bañuelos, Karen E.; Alcalá-Rmz, Vanessa; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi; Galván-Tejada, Carlos E.

doi:10.1007/978-3-031-21333-5_17

Cited by 1 publication

(2 citation statements)

References 18 publications

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“…Despite not clinching the title, VGG showcased the potential of deep convolutional networks, particularly through increased depth [74]. This BE review encompasses four studies involving this architecture [22,33,36,63,66]. ResNet, designed by Microsoft Research in 2015, became widely adopted due to its residual network design, which addressed the vanishing gradient problem in deep network training [75].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, Villagrana-Bañuelos, K.E., et al subdivided esophagitis and accomplished a four-classification task: normal vs. BE vs. esophagitis-a vs. esophagitis-b-d. They used 1561 endoscopic images from public databases to construct a model based on the VGG architecture, with the final model's AUC being normal: 0.95, BE: 0.96, esophagitis-a: 0.86, and esophagitis-b-d: 0.83 [36].…”

Section: Application Of Deep Learning To Assist Endoscopic Diagnosismentioning

confidence: 99%

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Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions

Cui,

Wang,

Lin

et al. 2023

Bioengineering

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Barrett’s esophagus (BE) represents a pre-malignant condition characterized by abnormal cellular proliferation in the distal esophagus. A timely and accurate diagnosis of BE is imperative to prevent its progression to esophageal adenocarcinoma, a malignancy associated with a significantly reduced survival rate. In this digital age, deep learning (DL) has emerged as a powerful tool for medical image analysis and diagnostic applications, showcasing vast potential across various medical disciplines. In this comprehensive review, we meticulously assess 33 primary studies employing varied DL techniques, predominantly featuring convolutional neural networks (CNNs), for the diagnosis and understanding of BE. Our primary focus revolves around evaluating the current applications of DL in BE diagnosis, encompassing tasks such as image segmentation and classification, as well as their potential impact and implications in real-world clinical settings. While the applications of DL in BE diagnosis exhibit promising results, they are not without challenges, such as dataset issues and the “black box” nature of models. We discuss these challenges in the concluding section. Essentially, while DL holds tremendous potential to revolutionize BE diagnosis, addressing these challenges is paramount to harnessing its full capacity and ensuring its widespread application in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of Deep Learning To Assist Endoscopic Diagnosismentioning

confidence: 99%

Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions

Cui,

Wang,

Lin

et al. 2023

Bioengineering

View full text Add to dashboard Cite

show abstract

Towards Esophagitis and Barret’s Esophagus Endoscopic Images Classification: An Approach with Deep Learning Techniques

Cited by 1 publication

References 18 publications

Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions

Deep Learning in Barrett’s Esophagus Diagnosis: Current Status and Future Directions

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