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Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP
Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often consid...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253103/ https://www.ncbi.nlm.nih.gov/pubmed/37296784 http://dx.doi.org/10.3390/diagnostics13111932 |
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author | Aldughayfiq, Bader Ashfaq, Farzeen Jhanjhi, N. Z. Humayun, Mamoona |
author_facet | Aldughayfiq, Bader Ashfaq, Farzeen Jhanjhi, N. Z. Humayun, Mamoona |
author_sort | Aldughayfiq, Bader |
collection | PubMed |
description | Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a “black box” that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model’s predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model’s predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment. |
format | Online Article Text |
id | pubmed-10253103 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-102531032023-06-10 Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP Aldughayfiq, Bader Ashfaq, Farzeen Jhanjhi, N. Z. Humayun, Mamoona Diagnostics (Basel) Article Retinoblastoma is a rare and aggressive form of childhood eye cancer that requires prompt diagnosis and treatment to prevent vision loss and even death. Deep learning models have shown promising results in detecting retinoblastoma from fundus images, but their decision-making process is often considered a “black box” that lacks transparency and interpretability. In this project, we explore the use of LIME and SHAP, two popular explainable AI techniques, to generate local and global explanations for a deep learning model based on InceptionV3 architecture trained on retinoblastoma and non-retinoblastoma fundus images. We collected and labeled a dataset of 400 retinoblastoma and 400 non-retinoblastoma images, split it into training, validation, and test sets, and trained the model using transfer learning from the pre-trained InceptionV3 model. We then applied LIME and SHAP to generate explanations for the model’s predictions on the validation and test sets. Our results demonstrate that LIME and SHAP can effectively identify the regions and features in the input images that contribute the most to the model’s predictions, providing valuable insights into the decision-making process of the deep learning model. In addition, the use of InceptionV3 architecture with spatial attention mechanism achieved high accuracy of 97% on the test set, indicating the potential of combining deep learning and explainable AI for improving retinoblastoma diagnosis and treatment. MDPI 2023-06-01 /pmc/articles/PMC10253103/ /pubmed/37296784 http://dx.doi.org/10.3390/diagnostics13111932 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Aldughayfiq, Bader Ashfaq, Farzeen Jhanjhi, N. Z. Humayun, Mamoona Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title | Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title_full | Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title_fullStr | Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title_full_unstemmed | Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title_short | Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP |
title_sort | explainable ai for retinoblastoma diagnosis: interpreting deep learning models with lime and shap |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10253103/ https://www.ncbi.nlm.nih.gov/pubmed/37296784 http://dx.doi.org/10.3390/diagnostics13111932 |
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