Automated detection of Malignant Lesions in the ovary using deep learning models and XAI

Abstract

Cancer is a complex and highly invasive disease that forms due to the abnormal growth of cells in any part of the body. A majority of cancers are unraveled and treated by incorporating advanced technology. However, ovarian cancer remains a dilemma as it has inaccurate non-invasive detection and a time consuming and invasive procedure for accurate detection. 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. Thus, in this research, several Convolutional Neural Networks such as LeNet-5, ResNet, VGGNet and GoogLeNet/Inception have been utilized to develop a model that accurately detects and identifies ovarian cancer. For effective model training, the dataset OvarianCancer&SubtypesDatasetHistopathology from Mendeley has been used. After selecting a base model, we utilized 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 base model, Accuracy, Precision, Recall, F1-Score and ROC Curve/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 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.