Classification of chest CT scans using convolutional autoencoders and local binary patterns
Year: 2025
Type: article
Abstract: Caused by an uncontrolled growth of cells in the lung, lung cancer is one of the leading causes of death around the world. This study introduces a novel computer-aided diagnosis system to classify chest CT-scans into two classes i.e. malignant and non-malignant. We train a convolutional autoencoder to extract deep features from input images and we combine the result with descriptors generated using local binary patterns. The concatenated vector is fed into a one-dimensional convolutional neural network to perform the classification task. The proposed model outperformed all other models in the literature while being tested on a subset of the IQ-OTHNCCD lung cancer dataset.
Author: Louai Zaiter
Institution: Aberystwyth University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410245025