The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning
This work addresses disease classification for medical imaging, but it appears incremental as it combines existing neural network methods without introducing a new paradigm.
The paper tackled disease detection in the gastrointestinal tract by proposing a system using global features and deep neural networks, achieving results such as 95.80% accuracy and 95.87% precision.
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%.