The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning

TL;DR

This paper introduces a deep learning system for classifying gastrointestinal diseases, achieving high accuracy by combining global features and neural networks in the Medico-Task 2018 challenge.

Contribution

It presents a novel approach that integrates global features with deep neural networks for disease detection in the gastrointestinal tract.

Findings

Achieved 95.80% accuracy

Attained 95.87% precision

F1-score of 95.80%

Abstract

In this paper, we present our approach for the 2018 Medico Task classifying diseases in the gastrointestinal tract. We have proposed a system based on global features and deep neural networks. The best approach combines two neural networks, and the reproducible experimental results signify the efficiency of the proposed model with an accuracy rate of 95.80%, a precision of 95.87%, and an F1-score of 95.80%.