Classification of CT scan and X-ray dataset based on deep learning and particle swarm optimization.

ABSTRACT: In 2019, the novel coronavirus swept the world, exposing the monitoring and early warning problems of the medical system. Computer-aided diagnosis models based on deep learning have good universality and can well alleviate these problems. However, traditional image processing methods may lead to high false positive rates, which is unacceptable in disease monitoring and early warning. This paper proposes a low false positive rate disease detection method based on COVID-19 lung images and establishes a two-stage optimization model. In the first stage, the model is trained using classical gradient descent, and relevant features are extracted; in the second stage, an objective function that minimizes the false positive rate is constructed to obtain a network model with high accuracy and low fa

SUBMITTER: Liu H 

PROVIDER: S-EPMC11771893 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature