An Adaptive Cycle-GAN-Based Augmented LIME-Enabled Multi-Stage Transfer Learning Model for Improving Breast Tumor Detection Using Ultrasound Images

Breast cancer is recognized as an aggressive cancer with the highest rate of mortality. Ultrasound imaging is a non-invasive and cost-effective strategy which is most frequently utilized in clinical methods. Especially, in ultrasound scan, breast tumors may appear in blurred and unclear boundaries. Thus, there is a necessity to improve the quality of breast ultrasound images. In this work, we introduce a cycle generative adversarial network (GAN) for translating noisy breast ultrasound images to denoised images. Furthermore, translating denoised images to reconstructed images helps in preserving breast tumor boundaries for better efficacy. To accurately identify the augmented breast tumor images, we consider an ensemble model of pre-trained transfer learning models such as Inception-v3, Densenet121, and XceptionLike. Furthermore, we present an automated boundary extraction using Local Interpretable Model-agnostic Explanations (LIME), providing interpretability for boundary extraction in breast lesions from ultrasound images. Through experimentation, we have achieved 93% of accuracy for the proposed model, and LIME provides better interpretability for each pre-trained model. Furthermore, the proposed model outperforms Vison Transformer (ViT) models.