ConvMixer-UNet: A Lightweight Network for Breast Lesion Segmentation in Ultrasound Images
Sara AbdElhakem, Sohier Basiony, Marwan Torki · 2023
Breast cancer is one of the most common types of cancer among women. It occurs when abnormal cells in the breast grow and divide uncontrollably. Early diagnosis and treatment are crucial in preventing its spread to the rest of the body. In this paper, we propose a ConvMixer-UNet network for ultrasound image segmentation. The objective is to identify the lesion in the ultrasound image. We design our network that consists of convolutional layers at the early level and ConvMixer layers at the latent level. ConvMixer is an extremely simple and parameter-efficient module that incorporates depthwise and pointwise convolutional layers. This model was evaluated using a breast ultrasound dataset (BUSI); it achieved an improvement in the value of Intersection over Union (IoU). We achieved 68.17% IoU and 80.60% Dice score. These scores are obtained via careful tuning for the network hyperparameters. Quantitative and qualitative comparisons ensure the value of our proposed network. Moreover, ConvMixer-UNet is considered a lightweight network compared to the leading medical segmentation network UNet and its extensions. We show that our network provides a significant reduction in the number of parameters to only 1.77 M parameters, in contrast to UNet which has 31.1 M parameters.