Barrett's esophagus analysis using infinity restricted Boltzmann machines

Made available in DSpace on 2019-10-06T17:02:09Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-01Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Fundação para o Desenvolvimento da UNESP (FUNDUNESP)The number of patients with Barret's esophagus (BE) has increased in the last decades. Considering the dangerousness of the disease and its evolution to adenocarcinoma, an early diagnosis of BE may provide a high probability of cancer remission. However, limitations regarding traditional methods of detection and management of BE demand alternative solutions. As such, computer-aided tools have been recently used to assist in this problem, but the challenge still persists. To manage the problem, we introduce the infinity Restricted Boltzmann Machines (iRBMs) to the task of automatic identification of Barrett's esophagus from endoscopic images of the lower esophagus. Moreover, since iRBM requires a proper selection of its meta-parameters, we also present a discriminative iRBM fine-tuning using six meta-heuristic optimization techniques. We showed that iRBMs are suitable for the context since it provides competitive results, as well as the meta-heuristic techniques showed to be appropriate for such task.UFSCAR – Federal University of São Carlos Department of ComputingMedizinische Klinik – Klinikum Augsburg IIIOTH Regensburg – Ostbayerische Technische Hochschule Regensburg Regensburg Medical Image Computing (ReMIC)OTH Regensburg – Regensburg Center of Health Sciences and Technology (RCHST)UNESP – São Paulo State University Department of ComputingUNESP – São Paulo State University Department of ComputingFAPESP: #2013/07375-0FAPESP: #2014/12236-1FAPESP: #2014/16250-9FAPESP: #2015/25739-4FAPESP: #2016/21243-7CNPq: #306166/2014-3CNPq: #307066/2017-7FUNDUNESP: 2597.201