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.