A robust encoder decoder based weighted segmentation and dual staged feature fusion based meta classification for breast cancer utilizing ultrasound imaging

Ultrasound imaging has become one of the most frequently employed modalities to detect and classify breast irregularities, which is a relatively cost-effective and important complement to mammography. To assist radiologists in locating worrisome lesions and improving the accuracy of diagnosis, a computer-aided diagnosis system is proposed which incorporates the knowledge of Generative Adversarial Network (GAN), weighted average based ensemble technique, and feature fusion based ensemble technique. After performing encoder decoder based lesion segmentation incorporating weighted ensemble architecture, a dual-staged feature fusion-based stacked ensemble meta-classifier architecture is employed for the final classification where three deep neural network branches are employed, and the generated feature maps from those branches were fused and fed to the fully connected network to achieve the final diagnosis result. The residual learning architecture and the pretrained foundation made the system faster, whereas compound scaling and ensemble architecture boosted the overall performance. The proposed methodology is evaluated on the BUSI, UDIAT, and Thammasat datasets. The Dice score reached to 0.8397, and the IoU score reached to 0.7482 in segmentation on the benign lesions of BUSI dataset whereas the classifier achieved a highest accuracy of 99.7%, F1-score of 99.7%, and AUC score of 0.999 in classification on the BUSI dataset. The results on the UDIAT and Thammasat datasets also indicate that our proposed method shows better performance than other methods. Thus, the proposed architecture can be considered for easy and automated diagnosis purposes.