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

2026-03-12

AI Summary

This study addresses the critical need for efficient, non-invasive early detection of ovarian cancer, as current diagnostic methods are invasive and time-consuming. The authors systematically develop and evaluate 15 convolutional neural network models based on established architectures—including LeNet-5, ResNet, VGGNet, and Inception—for classifying ovarian cancer histopathological images. For the first time in this domain, they comprehensively compare the explanatory efficacy of three explainable artificial intelligence (XAI) techniques: LIME, Integrated Gradients, and SHAP. Experimental results demonstrate that an optimized, compact InceptionV3 model achieves an average accuracy of 94% on an augmented dataset, significantly enhancing both diagnostic precision and model interpretability. These findings offer a reliable foundation for clinical decision support in ovarian cancer diagnosis.

Technology Category

Humans and AI: Explainable AI (XAI) for Human UnderstandingComputer Vision: Interpretability, Explainability, and TransparencyMachine Learning: Transparent, Interpretable, Explainable ML

Application Category

Web Mining and Content Analysis: Large pretrained models with web dataUser Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalizationSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• ovarian cancer
• malignant lesions
• non-invasive detection
• diagnostic accuracy
• medical imaging

Innovation

• Deep Learning
• Explainable AI (XAI)
• Ovarian Cancer Detection
• Convolutional Neural Networks
• Model Interpretability