Segmenting Benign and Malignant Solid Masses in Ultrasound Images Using Deep Learning

This study presents a novel approach for the segmentation of solid masses in ultrasound images, integrating preprocessing with detection and segmentation. Two convolutional neural networks, namely DeepLabv3+ and Darknet-53, are employed to identify regions of interest and to segment solid masses in ultrasound images. Furthermore, a preprocessing step utilizing the SRAD filter is applied prior to the image, whereby the edges of the masses are enhanced and the image noise is reduced. The strength of the methodology lies in its use of the YOLOv3 algorithm to delineate the region of interest and enhance the precision of the segmentation process. The results demonstrate a notable level of accuracy in the segmentation of both benign and malignant masses, with a Jaccard index of 90.10% and a Dice index of 94.71%. These outcomes exceed those achieved by existing techniques, substantiating the superiority of the proposed method for the analysis of ultrasound images.