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Residual-SwishNet: a deep learning-based approach for reliable lung cancer classification.

Marriam Nawaz¹, Ali Javed¹, Abdul Khader Jilani Saudagar²

¹Department of Software Engineering, University of Engineering and Technology-Taxila, Taxila, Pakistan.

Frontiers in Oncology

|February 11, 2026

Summary

This summary is machine-generated.

This study introduces Residual-SwishNet, a novel deep learning model for lung cancer classification. The model achieves high accuracy, offering a robust tool for early lung cancer diagnosis and improved patient outcomes.

Keywords:

ResNet50 Swish classification deep learning lung cancer

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Published on: September 25, 2021

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Lung cancer is a leading cause of global cancer deaths, necessitating improved diagnostic tools.
Current computer-aided detection systems face challenges in feature extraction, accuracy, and generalizability.

Purpose of the Study:

To develop a deep learning model for accurate and reliable lung cancer classification.
To enhance feature extraction and classification performance for lung cancer detection.

Main Methods:

A modified ResNet50 architecture, Residual-SwishNet, was developed.
The ReLU activation function was replaced with Swish, and three dense layers were added for enriched feature representation.
Softmax output with Cross-Entropy Loss was used to address class imbalance.

Main Results:

Residual-SwishNet achieved high classification accuracies of 99.60% on LUNA16 and 99.11% on IQ-OTH/NCCD.
The model demonstrated superior performance compared to existing state-of-the-art techniques.

Conclusions:

The proposed Residual-SwishNet model shows significant potential as a robust tool for lung cancer diagnosis.
This deep learning approach offers improved accuracy and reliability for early lung cancer detection.