Authors:
Alexandre Pessoa
1
;
Darlan Quintanilha
1
;
João Dallyson Sousa de Almeida
1
;
Geraldo Braz Junior
1
;
Anselmo C. de Paiva
1
and
António Cunha
2
Affiliations:
1
Núcleo de Computacão Aplicada, Universidade Federal do Maranhão (UFMA), São Luís, MA, Brazil
;
2
Universidade de Trás-os-Montes e Alto Douro (UTAD), Vila Real, Portugal
Keyword(s):
Endoscopy, Wireless Capsule Endoscopy, Deep Learning, EfficientNet.
Abstract:
The gastrointestinal tract is part of the digestive system, fundamental to digestion. Digestive problems can be symptoms of chronic illnesses like cancer and should be treated seriously. Endoscopic exams in the tract make detecting these diseases in their initial stages possible, enabling an effective treatment. Modern endoscopy has evolved into the Wireless Capsule Endoscopy procedure, where patients ingest a capsule with a camera. This type of exam usually exports videos up to 8 hours in length. Support systems for specialists to detect and diagnose pathologies in this type of exam are desired. This work uses a rarely used dataset, the ERS dataset, containing 121.399 labelled images, to evaluate three models from the EfficientNet family of architectures for the binary classification of Endoscopic images. The models were evaluated in a 5-fold cross-validation process. In the experiments, the best results were achieved by EfficientNetB0, achieving average accuracy and F1-Score of, re
spectively, 77.29% and 84.67%.
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