Bridging explainable machine vision in CAD Systems for lung cancer detection

Nusaiba Alwarasneh, Yuen Shan Serene Chow, Sarah Teh Mei Yan, Chern Hong Lim

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

2 Citations (Scopus)

Abstract

Computer-aided diagnosis (CAD) systems have grown increasingly popular with aiding physicians in diagnosing lung cancer using medical images in recent years. However, the reasoning behind the state-of-the-art black-box learning and prediction models has become obscured and this resultant lack of transparency has presented a problem whereby physicians are unable to trust the results of these systems. This motivated us to improve the conventional CAD with a more robust and interpretable algorithms to produce a system that achieves high accuracy and explainable diagnoses of lung cancer. The proposed approach uses a novel image processing pipeline to segment nodules from lung CT scan images, and then classifies the nodule using both 2D and 3D Alexnet models that have been trained on lung nodule data from the LIDC-IDRI dataset. The explainability aspect is approached from two angles: 1) LIME that produces a visual explanation of the diagnosis, and 2) a rule-based system that produces a text-based explanation of the diagnosis. Overall, the proposed algorithm has achieved better performance and advance the practicality of CAD systems.

Original languageEnglish
Title of host publicationIntelligent Robotics and Applications
Subtitle of host publication13th International Conference, ICIRA 2020, Proceedings
EditorsChee Seng Chan, Hong Liu, Xiangyang Zhu, Chern Hong Lim, Xinjun Liu, Lianqing Liu, Kam Meng Goh
Place of PublicationCham Switzerland
PublisherSpringer
Pages254-269
Number of pages16
ISBN (Electronic)9783030666453
ISBN (Print)9783030666446
DOIs
Publication statusPublished - 2020
EventInternational Conference on Intelligent Robotics and Applications 2020 - Kuala Lumpur, Malaysia
Duration: 5 Nov 20207 Nov 2020
Conference number: 13th
https://link.springer.com/book/10.1007/978-3-030-66645-3 (Proceedings)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume12595
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Intelligent Robotics and Applications 2020
Abbreviated titleICIRA 2020
Country/TerritoryMalaysia
CityKuala Lumpur
Period5/11/207/11/20
Internet address

Keywords

  • Cancer detection
  • Deep learning
  • Explainable AI
  • Image processing
  • Machine vision

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