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Explainable Information Retrieval using Deep Learning for Medical images

Apoorva Singh¹, Husanbir Singh Pannu¹ and Avleen Malhi²

1. Thapar Institute of Engineering and Technology
   Patiala India 147004
   apoorvasingh.singh1993@gmail.com, hspannu@thapar.edu
2. Bournemouth University
   Fern Barrow, Poole BH12 5BB, UK
   amalhi@bournemouth.ac.uk

Abstract

Image segmentation is useful to extract valuable information for an efficient analysis on the region of interest. Mostly, the number of images generated from a real life situation such as streaming video, is large and not ideal for traditional segmentation with machine learning algorithms. This is due to the following factors (a) numerous image features (b) complex distribution of shapes, colors and textures (c) imbalance data ratio of underlying classes (d) movements of the camera, objects and (e) variations in luminance for site capture. So, we have proposed an efficient deep learning model for image classification and the proof-of-concept has been the case studied on gastrointestinal images for bleeding detection. The Explainable Artificial Intelligence (XAI) module has been utilised to reverse engineer the test results for the impact of features on a given test dataset. The architecture is generally applicable in other areas of image classification. The proposed method has been compared with state-of-the-art including Logistic Regression, Support Vector Machine, Artificial Neural Network and Random Forest. It has reported F1 score of 0.76 on the real world streaming dataset which is comparatively better than traditional methods.

Key words

machine learning, explainable AI, image processing, medical images, capsule endoscopy

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS201030049S

Publication information

Volume 19, Issue 1 (January 2022)
Year of Publication: 2022
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Singh, A., Pannu, H. S., Malhi, A.: Explainable Information Retrieval using Deep Learning for Medical images. Computer Science and Information Systems, Vol. 19, No. 1, 277-307. (2022), https://doi.org/10.2298/CSIS201030049S