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This paper explores the use of deep learning models for automated detection of malignant ovarian lesions, aiming to improve diagnostic accuracy and reduce reliance on invasive procedures. Fifteen variants of CNN architectures, including LeNet-5, ResNet, VGGNet, and Inception, were trained on the OvarianCancer&SubtypesDatasetHistopathology. The InceptionV3 model with ReLU activation achieved the best performance, with an average score of 94% across performance metrics, and was further analyzed using LIME, Integrated Gradients, and SHAP for explainability.
InceptionV3 can detect ovarian cancer with 94% accuracy, offering a less invasive alternative to current diagnostic methods.
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 $\mathbf{9 4 \%}$ 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.
