UNet++ and LSTM combined approach for Breast Ultrasound Image Segmentation

TL;DR

This paper enhances breast ultrasound image segmentation by integrating LSTM and self-attention into UNet++ with multiscale features, achieving high accuracy and robustness on the BUSI dataset.

Contribution

It introduces a novel combined approach that incorporates LSTM layers and self-attention mechanisms into UNet++, along with multiscale feature extraction, for improved breast ultrasound segmentation.

Findings

Achieved 98.88% accuracy on BUSI dataset.

Attained 99.53% specificity and 95.34% precision.

Demonstrated competitive performance with state-of-the-art methods.

Abstract

Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature.