Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer
Article
Auzine, M., Heenaye-Mamode Khan, M., Baichoo, S., Gooda Sahib, N., Bissoonauth-Daiboo, P., Gao, X. and Heetun, Z. 2024. Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer. PLoS ONE. 19 (6), p. e0305628. https://doi.org/10.1371/journal.pone.0305628
Type | Article |
---|---|
Title | Development of an ensemble CNN model with explainable AI for the classification of gastrointestinal cancer |
Authors | Auzine, M., Heenaye-Mamode Khan, M., Baichoo, S., Gooda Sahib, N., Bissoonauth-Daiboo, P., Gao, X. and Heetun, Z. |
Abstract | The implementation of AI assisted cancer detection systems in clinical environments has faced numerous hurdles, mainly because of the restricted explainability of their elemental mechanisms, even though such detection systems have proven to be highly effective. Medical practitioners are skeptical about adopting AI assisted diagnoses as due to the latter's inability to be transparent about decision making processes. In this respect, explainable artificial intelligence (XAI) has emerged to provide explanations for model predictions, thereby overcoming the computational black box problem associated with AI systems. In this particular research, the focal point has been the exploration of the Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) approaches which enable model prediction explanations. This study used an ensemble model consisting of three convolutional neural networks(CNN): InceptionV3, InceptionResNetV2 and VGG16, which was based on averaging techniques and by combining their respective predictions. These models were trained on the Kvasir dataset, which consists of pathological findings related to gastrointestinal cancer. An accuracy of 96.89% and F1-scores of 96.877% were attained by our ensemble model. Following the training of the ensemble model, we employed SHAP and LIME to analyze images from the three classes, aiming to provide explanations regarding the deterministic features influencing the model's predictions. The results obtained from this analysis demonstrated a positive and encouraging advancement in the exploration of XAI approaches, specifically in the context of gastrointestinal cancer detection within the healthcare domain. |
Keywords | Gastrointestinal cancers; Artificial intelligence ; Ulcerative colitis; Cancer detection and diagnosis; Colorectal cancer; Endoscopy; Gastric cancer; Decision making |
Sustainable Development Goals | 3 Good health and well-being |
Middlesex University Theme | Health & Wellbeing |
Research Group | Computer Science |
Publisher | Public Library of Science |
Journal | PLoS ONE |
ISSN | |
Electronic | 1932-6203 |
Publication dates | |
Online | 25 Jun 2024 |
25 Jun 2024 | |
Publication process dates | |
Submitted | 24 Jan 2024 |
Accepted | 02 Jun 2024 |
Deposited | 14 Aug 2024 |
Output status | Published |
Publisher's version | License File Access Level Open |
Copyright Statement | © 2024 Auzine et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Digital Object Identifier (DOI) | https://doi.org/10.1371/journal.pone.0305628 |
PubMed ID | 38917159 |
PubMed Central ID | PMC11198752 |
National Library of Medicine ID | 101285081 |
https://repository.mdx.ac.uk/item/163617
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