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Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Geethu Lakshmi G¹, P Nagaraj¹

¹Department of Computer Science and Engineering, Kalasalingam Academy of Research and Education, Krishnankoil, Srivilliputhur, Tamil Nadu, India.

Computational Biology and Chemistry

|March 30, 2025

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A new deep learning model, Squeeze-Inception-ResNeXt, accurately classifies lung diseases from CT scans. This advanced system improves early lung cancer detection, aiding radiologists and potentially reducing mortality rates.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung cancer is a leading global cause of mortality, necessitating improved early detection and diagnostic methods.
Computer-Aided Diagnostic (CAD) systems assist radiologists in identifying lung nodules and malignancies, mitigating human error.
Accurate classification of lung cancer subtypes is critical for effective treatment strategies and patient outcomes.

Purpose of the Study:

To develop and evaluate a deep learning approach for classifying lung diseases using chest Computed Tomography (CT) images.
To enhance the accuracy and efficiency of lung disease diagnosis through advanced image processing and classification techniques.
To introduce a novel deep learning architecture, Squeeze-Inception-ResNeXt, for improved lung cancer subtype classification.

Keywords:

CT-scan Deep learning Feature extraction Lung cancer Optimization algorithm

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Main Methods:

Image pre-processing techniques including color space conversion, data augmentation, resizing, and normalization were applied to CT scans.
Feature extraction was performed using a Convolutional Neural Network (CNN) optimized with the Slime Mould Algorithm (SMA).
A hybrid classification model, Squeeze-Inception-ResNeXt, combining Squeeze-Inception V3 and ResNeXt, was developed and trained using SMA.

Main Results:

The Squeeze-Inception-ResNeXt model achieved high performance in classifying lung diseases, including Adenocarcinoma, Large Cell Carcinoma, and Squamous Cell Carcinoma.
The proposed model demonstrated superior accuracy (97.7%), sensitivity (98.1%), and specificity (97.4%) compared to traditional models.
The Squeeze-Inception-ResNeXt model offers reduced computational cost while maintaining high diagnostic performance.

Conclusions:

The developed deep learning approach, Squeeze-Inception-ResNeXt, shows significant promise for accurate and efficient lung disease classification from CT scans.
This method has the potential to aid radiologists in early lung cancer detection and diagnosis, contributing to improved patient management.
The integration of SMA for optimization further enhances the model's effectiveness in clinical applications.