One-Stage Classifiers Based on U-Net and Autoencoder with Attention for Recognition of Neoplasms from Single-Channel Monochrome Computed Tomography Images
Aleksei Samarin, Alexander Savelev, Aleksei Toropov, Alina Dzestelova, Valentin Malykh, E. Mikhailova, Alexander Motyko
Abstract
Abstract Currently, one of the most useful methods for diagnosing lung cancer is based on computed tomography. In addition, online medical consultation services are becoming increasingly popular. The article presents two architectures based on neural networks with an attention mechanism for recognizing lung cancer from a single computed tomography image. One of the proposed approaches is an optimization of a previously presented two-stage model that has demonstrated state-of-the-art results on the Open Joint Monochrome Lungs Computer Tomography (OJMLCT) dataset first proposed by Samarin et al. [13]. Our solution allowed us to reduce the number of model parameters without significant loss of classification efficiency.