Classification of anatomical landmarks in the gastrointestinal tract with endoscopy images utilizing convolutional neural networks and triplet loss

• Lisle Faray de Paiva UFMA
• Alan Carlos de Moura Lima UFMA
• Geraldo Braz Júnior UFMA
• Anselmo Cardoso de Paiva UFMA
• Aristófanes Correa Silva UFMA

DOI: https://doi.org/10.5753/eniac.2021.18280

Abstract

According to the World Health Organization, 8 million deaths are counted due to gastrointestinal diseases annually. Automatic detection of gastrointestinal landmarks is a task that can further help medical professionals reducing cost and time in exploratory exams. Computer-aided detection and diagnosis systems have been widely explored in the scientific field. However, it takes a lot of processing power to achieve satisfactory results. In order to overcome this problem, this work uses a simple Convolutional Neural Network together with the Triplet Loss cost function to extract image characteristics of 3 gastrointestinal anatomical landmarks (z-line, pylorus, and cecum) to classify those images.For the training it's used the dataset Kvasir-v2, obtaining 96,60% of Precision, 97,71% of Accuracy, 96,91% of Specificity, 98,61% of Recall 97,59% of F1-score.

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