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Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification by AHMET ÇINAR is a document available to read on EtoBox.

What is Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification about?

This paper presents a novel methodology for the automatic detection and localization of gastrointestinal anomalies in endoscopic video frames using weakly annotated images. The approach employs a weakly supervised convolutional neural network (WCNN) for classification, a deep saliency detection algorithm for identifying salient points, and an iterative cluster unification (ICU) algorithm for localization, achieving high performance metrics. Experimental results demonstrate the methodology

Author

AHMET ÇINAR

Language

EN