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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images.

Roxana Zahedi Nasab¹, Hadis Mohseni¹, Mahdieh Montazeri²

¹Department of Computer Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

|February 26, 2024

Summary

This summary is machine-generated.

A new deep convolutional neural network, AFEX-Net, efficiently classifies lung diseases from chest CT scans. This AI tool aids in early detection and diagnosis, outperforming existing methods with faster training and high accuracy.

Keywords:

Convolutional neural network adaptive feature extraction chest computerized tomography images classification

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Area of Science:

Artificial Intelligence
Medical Imaging
Computer Science

Background:

Deep convolutional neural networks (CNNs) are crucial for medical image processing.
Emerging lung diseases like COVID-19 necessitate advanced diagnostic tools.
Early detection of lung diseases from chest CT scans is a significant research focus.

Purpose of the Study:

To introduce and evaluate AFEX-Net, an efficient CNN for classifying lung diseases.
To assess AFEX-Net's capability in distinguishing various lung indications from CT images.
To enable early-stage diagnosis of lung diseases using medical imaging.

Main Methods:

Designed AFEX-Net, a lightweight CNN with adaptive layers and functions.
Trained and tested AFEX-Net on over 10,000 chest CT slices (CC dataset).
Validated generalizability using the public COVID-CTset dataset and effective pre-processing.

Main Results:

AFEX-Net demonstrated high accuracy on both CC and COVID-CTset datasets.
Achieved a learning speed three times faster than comparable CNNs due to its lightweight design.
Successfully extracted distinguishing features for classifying lung diseases, particularly in early stages.

Conclusions:

AFEX-Net is a high-performing CNN for lung disease classification from chest CT images.
Its efficiency, adaptability, and compatibility make it a reliable tool for early detection.
AFEX-Net supports timely diagnosis and management of various lung conditions.