Arquivos de gastroenterologia 54(1):16–20
Experimental results of the proof-of-concept have been compared with the state-of-the-art techniques to demonstrate the performance improvement of the multi-modal system.īoal Carvalho P, Magalhães J, Dias de Castro F, Monteiro S, Rosa B, Moreira MJ, et al (2017) Suspected blood indicator in capsule endoscopy: a valuable tool for gastrointestinal bleeding diagnosis. Thus, the classification accuracy is further assisted by performing the sentiment analysis using Long Short Term Memory (LSTM) deep learning network and Bag of Words. After the image classification is performed, there is a certain complex family of images that often cannot be further classified. The case study has been performed on the binary classification of in-vivo gastral images and related text obtained from a known gastroenterologist. This article has proposed a framework to perform the sentiment analysis on the rich textual information available from the linguistic cues of related images and incorporate them to enhance image classification. These linguistic keywords can be used as additional “sensors” to enhance efficiency while acting as another mode of information.
With the ever-growing availability of multimedia data with the help of the Internet and social platforms, many images are available along with their collateral text. Image classification is a challenging problem and often suffers from the bottleneck of visual features.
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