One of the most recent non-invasive technologies to examine the gastrointestinal tract is wireless capsule endoscopy (WCE). As there are thousands of endoscopic images in an 8-15 h long video, an evaluator has to pay constant attention for a relatively long time (60-120 min). Therefore the possibility of the presence of pathological findings in a few images (displayed for evaluation for a few seconds only) brings a significant risk of missing the pathology with all negative consequences for the patient. Hence, manually reviewing a video to identify abnormal images is not only a tedious and time consuming task that overwhelms human attention but also is error prone. In this paper, a method is proposed for the automatic detection of abnormal WCE images. The differential box counting method is used for the extraction of fractal dimension (FD) of WCE images and the random forest based ensemble classifier is used for the identification of abnormal frames. The FD is a well-known technique for extraction of features related to texture, smoothness, and roughness. In this paper, FDs are extracted from pixel-blocks of WCE images and are fed to the classifier for identification of images with abnormalities. To determine a suitable pixel block size for FD feature extraction, various sizes of blocks are considered and are fed into six frequently used classifiers separately, and the block size of 7×7 giving the best performance is empirically determined. Further, the selection of the random forest ensemble classifier is also done using the same empirical study. Performance of the proposed method is evaluated on two datasets containing WCE frames. Results demonstrate that the proposed method outperforms some of the state-of-the-art methods with AUC of 85% and 99% on Dataset-I and Dataset-II respectively.

2nd Department of Internal Medicine Gastroenterology Charles University Faculty of Medicine in Hradec Kralove University Hospital Hradec Kralove Sokolska 581 Hradec Kralove 50005 Czech Republic

Artificial Intelligence Lab Oslo Metropolitan University 460167 Norway

Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradecka 1249 Hradec Kralove 50003 Czech Republic; Malaysia Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra 54100 Kuala Lumpur Malaysia

PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India

PDPM Indian Institute of Information Technology Design and Manufacturing Jabalpur 482005 India; Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradecka 1249 Hradec Kralove 50003 Czech Republic

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