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Updated: May 17, 2025

Author Spotlight: Advancements in Molecular Biomarker Testing for Non-Squamous Non-Small Cell Lung Cancer

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Published on: September 8, 2023

Classification of lung cancer severity using gene expression data based on deep learning.

Ali Bou Nassif¹, Nour Ayman Abujabal², Aya Alchikh Omar²

¹Department of Computer Engineering, College of Computing and Informatics, University of Sharjah, P.O Box: 27272, Sharjah, UAE. anassif@sharjah.ac.ae.

BMC Medical Informatics and Decision Making

|May 14, 2025

Summary

This summary is machine-generated.

This study introduces a Deep Learning (DL) model, a Convolutional Neural Network (CNN), for classifying lung cancer stages. The model achieved high accuracy in identifying Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC).

Keywords:

Classification Deep learning Feature selection Gene expression LUAD LUSC Lung cancer

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Area of Science:

Oncology
Bioinformatics
Computational Biology

Background:

Lung cancer is a leading cause of cancer-related mortality globally.
Machine Learning (ML) and Deep Learning (DL) show promise in cancer detection and classification.
Gene expression data offers potential for understanding and diagnosing lung cancer subtypes.

Purpose of the Study:

To develop and evaluate a Deep Learning (DL) model for classifying lung cancer stages.
To specifically address the challenges of class imbalance and overfitting in gene expression datasets for lung cancer.
To improve the accuracy of lung cancer subtype classification using gene data.

Main Methods:

A Convolutional Neural Network (CNN) model was designed for lung cancer stage classification.
The F-test feature selection method was employed to optimize the model.
Gene expression datasets for Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC) were utilized.
Medical and experimental analysis was performed to mitigate dataset challenges.

Main Results:

The optimized CNN model achieved high classification accuracy for lung cancer subtypes.
Approximately 93.94% accuracy was obtained for Lung Adenocarcinoma (LUAD).
Approximately 88.42% accuracy was achieved for Lung Squamous Cell Carcinoma (LUSC).

Conclusions:

Deep Learning, specifically CNNs, can effectively classify lung cancer stages using gene expression data.
The F-test feature selection method is beneficial for improving DL model performance in this context.
The proposed approach offers a promising tool for lung cancer diagnosis and research.