Medical Diagnostic by Data Bagging for Various Instances of Neural Network

Autor:	Muhammad Atif Tahir, Muhammad Usman Tariq Alvi, Shahbaz Memon, Zeshan Khan
Rok vydání:	2021
Předmět:	Medical diagnostic Majority rule Artificial neural network Computer science business.industry Deep learning education Artificial intelligence Transfer of learning business Feature learning
Zdroj:	Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687922 ICPR Workshops (8)
Popis:	Computer-aided diagnostics is helping the medical experts for fast diagnostics, using machine learning and representation learning techniques. Various types of diagnostics are using the assistance of machine learning approaches including endoscopy. In this paper, a transfer learning based bagging approach is investigated for endoscopy images analysis. Bagging is used to fine-tune several instances of the deep learning model with 70% of data in each bag. These all models of deep learning are combined to generate a single prediction using majority voting and neural-network-based decision approach. The best approach resulted in an F1-score of 0.60 on the EndoTech 2020 dataset having 23 abnormalities in the GI-Tract.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d08c361b27d22fbb94763017a6f9341f https://doi.org/10.1007/978-3-030-68793-9_21 Zobrazit plný text záznamu