EfficientU-Net: A Novel Deep Learning Method for Breast Tumor Segmentation and Classification in Ultrasound Images

Segmentation and classification of breast tumors in ultrasound (US) images is an important application to deep neural networks. Unet architecture is recently developed for image segmentation. However, the standard convolution used in the Unet architecture can lead to problems such as loss of information, capturing irrelevant features, and failing to accurately localize the boundary of the segmented parts in an image. Additionally, the model involves a large number of parameters, leading to high computational time. This work proposes a novel deep-learning method called EfficientU-Net for breast tumor segmentation in ultrasound (US) images. The method addresses the above challenges by integrating a modified EfficientNet with an atrous convolution (AC) block in the encoder part of the Unet model. The EfficientNet uses depth-wise separable convolution to minimize training parameters and capture relevant texture features to localize the tumor boundary accurately. The AC block handles the diversity in the shape and size of tumors. The classification network based on the pre-trained CNN architecture and fine-tuned using a custom neural network is developed. The classification network is used to classify the generated masks into benign, malignant, and normal classes. The performance of the proposed method is tested on two publicly available US image datasets, BUSI and dataset-B. The proposed method demonstrates a reduction in the number of parameters, a decrease in computational complexity, and a hike in inference speed while achieving superior performance in terms of segmentation and classification metrics.