Skip to main content
 

 

 

Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP

Author name : BADER MUNIF KHALAF ALDUGHAYFIQ
Publication Date : 2023-06-04
Journal Name : Diagnostics

Abstract

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.

Keywords

retinoblastoma; explainable AI; deep learning; LIME; SHAP; medical image analysis; InceptionV3; transfer learning

Publication Link

https://doi.org/10.3390/diagnostics13111932

Block_researches_list_suggestions

Suggestions to read

Photocurrent and electrical properties of SiGe Nanocrystals grown on insulator via Solid-state dewetting of Ge/SOI for Photodetection and Solar cells Applications
MOHAMMED OMAR MOHAMMEDAHMED IBRAHIM
Comparative analysis of high-performance UF membranes with sulfonated polyaniline: Improving hydrophilicity and antifouling capabilities for water purification
EBTSAM KHALEFAH H ALENEZY
Efficient framework for energy management of microgrid installed in Aljouf region considering renewable energy and electric vehicles
Ali fathy mohmmed ahmed
Comparative analysis of high-performance UF membranes with sulfonated polyaniline: Improving hydrophilicity and antifouling capabilities for water purification
AHMED HAMAD FARHAN ALANAZI
Contact