Connected ResU-Net: A Deep Learning Model for Segmentation of Breast Cancer Ultrasound Images

With Artificial Intelligence (AI)’s advancement in computer vision, AI-aided medical system has become a must, such as for breast cancerous mass detection. Moreover, to fight against this kind of deadly disease, detection in the early stage plays a vital role. A deep learning model can be trained to detect cancerous mass without wasting time. There is widespread agreement that efficient deep network training necessitates thousands of annotated training examples. With this concern, we experimented with the different deep learning models based on U-Net: traditional U-Net, ResU-Net, and Connected U-Net to compare and differentiate between the models for detecting breast cancerous tissue. By analyzing different U-Net variants, a novel U-Net architecture is developed, namely Connected ResU-Net, by combining the algorithm of ResU-Net and Connected U-Net algorithms. With this arrangement, the model is trained for 50 epochs, and to measure the accuracy, the Mean IOU (intersection over union) metric is used, which gives a Mean IOU of 72.50% for the training and 63.10% for the testing dataset.