An Efficient Model for Lung Cancer Detection through the Integration of Genetic Algorithm and Machine Learning
Received: 5 October 2024 | Revised: 30 October 2024 | Accepted: 3 November 2024 | Online: 11 November 2024
Corresponding author: Abdulaziz A. Alsulami
Abstract
Prompt lung cancer detection is essential for patient health. Deep Learning (DL) models have been intensively used for lung cancer screening, as they provide high accuracy in diagnoses. However, DL models require significant computational power, which may not be accessible in all settings. Conventional Machine Learning (ML) models may not produce high prediction accuracy, especially with large data. This study uses a Genetic Algorithm (GA) approach to select optimal features from lung cancer images and reduce their dimensionality. This allows conventional ML models to achieve a high prediction accuracy when classifying medical images while using lower computational power compared with DL models. The proposed model integrates GA along with ML for lung cancer detection. The experimental results show that using GA with a feed-forward neural network classifier achieved high performance, reaching 99.70% classification accuracy.
Keywords:
machine learning, image classification, genetic algorithm, deep learningDownloads
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