Data Augmentation and Assessment for Enhanced Ovarian Tumor Classification

Deep learning based automatic classification of benign and malignant ovarian tumors from ovarian ultrasound images brings many significant benefits in women’s health care. Nonetheless, due to privacy concerns associated with medical data, acquiring adequate data for training deep learning models is not straightforward, and sharing data for research purposes is not publicly feasible. Data augmentation helps address this issue by generating new diverse data from existing datasets. In this paper, we explore various data augmentation techniques, ranging from simple methods like geometric transforms and intensity adjustments to advanced techniques using generative AI models such as Deep Convolutional Generative Adversarial Networks (DCGAN). We then introduce a method to automatically assess the quality of generated images. The augmented data is then used to train four state-of-the-art image classification models: VGG16, ResNet34, DenseNet201 and Inception-v3. Experimental results achieved with the OTU2D dataset show an accuracy of 92.54%, increase of 1.92%. On the OvaTUS dataset, the recall reached 93.2%, increase of 5.29%. When combining the OTU2D and OvaTUS datasets, the accuracy was 91.69%, increase of 4.59%. Experimental results show that the generated images significantly improve the classification accuracy for all tested models on three datasets (OTU2D, OvaTUS, and combined OTU2D with OvaTUS).