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Esophageal Adenocarcinoma (EAC) is considered as the early stage of esophageal cancer developed mainly from the pre-malignant changes in esophagus lining named Barrett’s Esophagus (BE). Throughout the gastroin- testinal tract examination, premalignant and early cancer stages in the esoph- agus are usually overlooked as they are considered challenging to detect and requires a significant experience. Computer Aided Detection (CAD) systems, therefore, could be helpful in automatically detecting early cancerous lesions. With the recent advances in deep learning, the performance of object detec- tion methods has been increased to a great extent. In this paper, we aim to evaluate the performance of different state-of-the-art deep learning detection methods (RCNN, Fast, Rcnn, Faster RCNN, SSD) to automatically allocate BE abnormalities. To achieve that, a dataset of High-Definition white light en- doscopy images from 39 patients with corresponding manually annotated by five experienced clinicians has been evaluated. Experimental results show that Single Shor Multibox Detector (SSD) , outperforms other methods in terms of the evaluation measures

Pertenece a

Faculty of Technology ePrints Service  


Ghatwary, Noha -  Ye, Xujiong -  Zolgharni, Massoud - 

Id.: 71510627

Idioma: inglés  - 

Versión: 1.0

Estado: Final

Tipo:  application/pdf - 

Palabras claveG400 Computer Science - 

Tipo de recurso: Conference or Workshop contribution  -  NonPeerReviewed  - 

Tipo de Interactividad: Expositivo

Nivel de Interactividad: muy bajo

Audiencia: Estudiante  -  Profesor  -  Autor  - 

Estructura: Atomic

Coste: no

Copyright: sí

Formatos:  application/pdf - 

Requerimientos técnicos:  Browser: Any - 

Relación: [References] http://eprints.lincoln.ac.uk/32583/

Fecha de contribución: 06-jul-2018


* Ghatwary, Noha and Ye, Xujiong and Zolgharni, Massoud (2018) Evaluation of Early Esophageal Adenocarcinoma Detection Using Deep Learning. In: CARS 2018 Computer Assisted Radiology and Surgery, 20-27, June, 2018, Berlin.

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