Automated Detection of Malignant Lesions in the Ovary Using Deep Learning Models and XAI

TL;DR

This study develops and evaluates deep learning models, particularly InceptionV3, for accurate non-invasive ovarian cancer detection, and employs XAI techniques to interpret model decisions, aiming to improve diagnostic methods.

Contribution

Introduces a deep learning-based ovarian cancer detection model with explainability using XAI techniques, enhancing interpretability and accuracy over existing methods.

Findings

InceptionV3 achieved 94% accuracy and high performance metrics.

XAI methods provided valuable insights into model decision-making.

The model outperformed other CNN variants in detection accuracy.

Abstract

The unrestrained proliferation of cells that are malignant in nature is cancer. In recent times, medical professionals are constantly acquiring enhanced diagnostic and treatment abilities by implementing deep learning models to analyze medical data for better clinical decision, disease diagnosis and drug discovery. A majority of cancers are studied and treated by incorporating these technologies. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection procedures and a time consuming, invasive procedure for accurate detection. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop 15 variants and choose a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After constructing a model, we utilized Explainable Artificial Intelligence (XAI) models such as LIME, Integrated Gradients and SHAP to explain the black box outcome of the selected model. For evaluating the performance of the model, Accuracy, Precision, Recall, F1-Score, ROC Curve and AUC have been used. From the evaluation, it was seen that the slightly compact InceptionV3 model with ReLu had the overall best result achieving an average score of 94% across all the performance metrics in the augmented dataset. Lastly for XAI, the three aforementioned XAI have been used for an overall comparative analysis. It is the aim of this research that the contributions of the study will help in achieving a better detection method for ovarian cancer.