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Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours.

Muhammad Asim Saleem¹, Ngoc Thien Le¹, Widhyakorn Asdornwised¹

¹Center of Excellence in Artificial Intelligence, Machine Learning and Smart Grid Technology, Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand.

Sensors (Basel, Switzerland)

|February 28, 2023

Summary

This summary is machine-generated.

This study introduces a deep learning model using the sooty tern optimization algorithm (SHOA) for accurate non-small cell lung cancer (NSCLC) diagnosis. The model achieved 98.32% accuracy in identifying malignant lung nodules.

Keywords:

deep learning model lung cancer non-small cell lung cancer (NSCLC)sooty tern optimization algorithm (STOA)

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Lung cancer is a leading cause of cancer mortality globally.
Early detection of lung nodules is crucial for effective treatment and patient recovery.
Current methods struggle with accurate segmentation and classification of diverse lung cancer cell sizes.

Purpose of the Study:

To develop a highly accurate deep learning model for diagnosing non-small cell lung cancer (NSCLC) tumors.
To improve the accuracy and reliability of lung nodule segmentation and classification.

Main Methods:

A deep learning (DL) model incorporating the sooty tern optimization algorithm (SHOA) was proposed.
Otsu segmentation method was used for initial lung nodule isolation.
SHOA optimized feature selection for improved diagnostic accuracy.
Local binary pattern (LBP) was employed for feature extraction.
Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) classifiers were utilized for malignancy assessment.

Main Results:

The SHOA-optimized deep neural network (DNN) model achieved a diagnostic accuracy of 98.32%.
This performance surpassed that of baseline methods used for comparison.
The model demonstrated enhanced reliability in segmenting and classifying lung nodules.

Conclusions:

The proposed SHOA-optimized DL model offers a significant advancement in accurate NSCLC diagnosis.
This approach effectively addresses limitations of classical methods in segmenting and classifying lung nodules.
The high accuracy suggests potential for improved patient outcomes through earlier and more reliable lung cancer detection.