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

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

Figure 1

Comparison of a healthy eye (a) and a retinoblastoma eye (b) [2].

Figure 2

Internation Intraocular Retinoblastoma Classification [6].

Figure 3

Illustration of the Overall Framework.

Figure 4

Sample image from dataset: (a) retinoblastoma fundus and (b) normal fundus.

Figure 5

Architecture of transfer learning with InceptionV3 model for retinoblastoma classification, including LIME and SHAP visualizations.

Figure 6

Accuracy Curve for Training and Validation for the Initial Epochs.

Figure 7

Loss Curve for Training and Validation for the Initial Epochs.

Figure 8

Saliency Map of Retinoblastoma Image 1 Using LIME.

Figure 9

Saliency Map of Retinoblastoma Image 2 USING LIME.

Figure 10

Saliency Map of Retinoblastoma Image 1 USING SHAP.

Figure 11

Saliency Map of Retinoblastoma Image 2 USING SHAP.

Figure 12

Saliency Map of Retinoblastoma Image 3 USING SHAP.

Figure 13

Saliency Map of Normal Fundus Image 1 USING SHAP.

Figure 14

Saliency Map of Normal Fundus Image 2 USING SHAP.