U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation

Published in

arXiv.org(2020)

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TL;DR

U-Det, a resource-efficient model architecture, which is an end to end deep learning approach to solve the task at hand, incorporates a Bi-FPN (bidirectional feature network) between the encoder and decoder and uses Mish activation function and class weights of masks to enhance segmentation efficiency.

Abstract

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.

Authors

Nikhil Varma Keetha

1 papers

P. SamsonAnoshBabu

1 papers

Chandra Sekhara Rao Annavarapu

1 papers

References58 items

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U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation

Published in

arXiv.org(2020)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Nikhil Varma Keetha

1 papers

P. SamsonAnoshBabu

1 papers

Chandra Sekhara Rao Annavarapu

1 papers

References58 items

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The proposed U-Det model uses a bidirectional feature network, which functions as a feature enricher, integrating multi-scale feature fusion for efﬁcient feature extraction

Field of Study

Computer ScienceEngineeringMathematics

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv