A Comparative Study on Pre-Training Models of Deep Learning to Detect Lung Cancer

Authors

Keywords:

Deep Learning, VGG-16, Inception V3, ResNet50, Lung Cancer, Pre-trained Models

Abstract

Detection of lung cancer using neural network-based systems has seen a reasonable improvement. However, the possibility of false cancer detection seems to be a worrying factor in recent times due to various technical reasons. Recent research programs revealed that machine learning (ML) based techniques were also found to make a greater contribution to lung cancer detection. However, deep learning (DL) techniques seem to provide enhanced accuracy for various medical research areas. Therefore, in this work different types of DL pre-trained prediction models are tested to study the accuracy of each model. The pre-trained models are applied to the dataset consisting of nearly 5000 images consisting of cancerous and non-cancerous data. Particularly, VGG-16, Inception V3, and ResNet50 are the Transfer Learning models used in this study for comparative analysis. The results show a reasonable accuracy using the VGG-16 model with fine-tuning and the image augmentation obtained greater accuracy of 96% and 93% for training data and validation data respectively.

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VGG-16 Model Architecture

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Published

16.01.2023

How to Cite

Aluka, M. ., Ganesan, S. ., & Reddy P., V. P. . (2023). A Comparative Study on Pre-Training Models of Deep Learning to Detect Lung Cancer. International Journal of Intelligent Systems and Applications in Engineering, 11(1), 148–155. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2453

Issue

Vol. 11 No. 1 (2023)

Section

Research Article

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