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WorldCist'20 - 8th World Conference on Information Systems and Technologies

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Colorectal Image Classification with Transfer Learning and Auto-adaptive Artificial Intelligence Platform

In automatic (computer-based) interpretation of medical images, the use of deep learning techniques is limited because of the lack of large publicly available datasets. With just hundreds of samples (images) in a dataset, the application of deep learning techniques is very hard, and the results are under expectations. Training a multi layer convolutional neural network requires thousands or even millions of images for an acceptable level of correct classification. In this paper we will present a novel approach that can be used to solve computer vision related problems (e.g. medical image processing) even when only a small dataset of images are available for training. We will show that combining Transfer Learning and some auto-adaptive artificial intelligence algorithms we can obtain very good classification rates even with the use of a limited dataset. As a demonstration of the effectiveness of our approach we will show the use of this technique to solve the polyp detection problem in endoscopic image sets. We show that using just a subset of the available images (from the original dataset containing 4000 images) the results are comparable with the case when all the images were used.

Zoltan Czako
Technical University of Cluj-Napoca
Romania

Anca Hangan
Technical University of Cluj-Napoca
Romania

Gheorghe Sebestyen-Pal
Technical University of Cluj-Napoca
Romania

 


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