A Generalized Deep Learning Model for Multi-disease Chest X-Ray Diagnostics

Nabit Bajwa, Kedar Bajwa, Muhammad Faique Shakeel, Atif Rana, Kashif Haqqi, Suleiman Khan

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

We investigate the generalizability of deep convolutional neural network (CNN) on the task of disease classification from chest x-rays collected over multiple sites. We systematically train the model using datasets from three independent sites with different patient populations: National Institute of Health (NIH), Stanford University Medical Centre (CheXpert), and Shifa International Hospital (SIH). We formulate a sequential training approach and demonstrate that the model produces generalized prediction performance using held out test sets from the three sites. Our model generalizes better when trained on multiple datasets, with the CheXpert-Shifa-NET model performing significantly better (p-values < 0.05) than the models trained on individual datasets for 3 out of the 4 distinct disease classes.

Originalspråkengelska
Titel på värdpublikationAdvances in Computational Intelligence - 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Proceedings
RedaktörerIgnacio Rojas, Gonzalo Joya, Andreu Catala
Antal sidor12
FörlagSpringer Science and Business Media Deutschland GmbH
Utgivningsdatum2023
Sidor541-552
ISBN (tryckt)9783031430848
DOI
StatusPublicerad - 2023
MoE-publikationstypA4 Artikel i en konferenspublikation
Evenemang17th International Work-Conference on Artificial Neural Networks, IWANN 2023 - Ponta Delgada, Portugal
Varaktighet: 19 juni 202321 juni 2023

Publikationsserier

NamnLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volym14134 LNCS
ISSN (tryckt)0302-9743
ISSN (elektroniskt)1611-3349

Bibliografisk information

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Vetenskapsgrenar

  • 217 Medicinsk teknik

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