Breast Tumor Recognition by Semantic Segmentation of Multiclass Ultrasound Images

Abstract

Objectives: The main purpose of this paper is to suggest a semantic segmentation model to reduce training time in ultrasound breast cancer images. This is achieved by employing a smaller network with fewer trainable parameters, resulting in faster training while maintaining maximum accuracy.
 Methods: This paper proposes a modified U-Net model, which we call the V model, for the subdivision of breast tumors. The proposed V architecture is applied explicitly to ultrasound breast cancer datasets for semantic segmentation. Our proposed model achieves semantic segmentation by employing an encoder and decoder on real and mask image datasets.
 Findings: Therefore, developing a proposed system, namely a V-Net computer-aided diagnosis (CAD) system, is imperative. This CAD system aims to minimize human errors while enhancing accuracy and speed in the premature finding of breast tumors. The proposed model utilizes minimal layers and parameters while maintaining superior results regarding correctness, speed, and computational proficiency.
 Novelty: The proposed V-net model applies to analysing any medical image for detecting disease and finding more accuracy than other U-net models.