  Early diagnosis and analysis of lung cancer involve a precise and efficient
lung nodule segmentation in computed tomography (CT) images. However, the
anonymous shapes, visual features, and surroundings of the nodule in the CT
image pose a challenging problem to the robust segmentation of the lung
nodules. This article proposes U-Det, a resource-efficient model architecture,
which is an end to end deep learning approach to solve the task at hand. It
incorporates a Bi-FPN (bidirectional feature network) between the encoder and
decoder. Furthermore, it uses Mish activation function and class weights of
masks to enhance segmentation efficiency. The proposed model is extensively
trained and evaluated on the publicly available LUNA-16 dataset consisting of
1186 lung nodules. The U-Det architecture outperforms the existing U-Net model
with the Dice similarity coefficient (DSC) of 82.82% and achieves results
comparable to human experts.
