Clinical Informatics System to Understand Lung Cancer Using Transfer Learning-Based Computer Aided Application-I

Document Type

Conference Proceeding

Source of Publication

2023 International Conference on Technology, Engineering, and Computing Applications (ICTECA)

Publication Date

12-22-2023

Abstract

Significant advancements in diagnostic and sequencing technologies have propelled lung cancer clinical studies forward. The focal point of these endeavors has been the early detection of lung cancer, leading to the development of various computer-aided diagnostic tools powered by Artificial Intelligence applications and deep learning techniques. However, it is note- worthy that some deep learning techniques are susceptible to overfitting, which can compromise their effectiveness in practice. Addressing the challenge of accurately categorizing lung cancer is crucial. In this study, we introduced a CNN-based classification method designed to distinguish between lung adenocarcinoma and benign lung conditions. To enhance model performance, we incorporated batch normalization into our proposed approach, which yielded impressive results across both binary categories, achieving an accuracy rating of 97.92%. In our comparative analysis with other deep learning-based methods, our approach consistently demonstrated superiority in performance. This underscores the potential of our model as a valuable tool in the accurate categorization of lung cancer.

ISBN

979-8-3503-6055-4

Publisher

IEEE

Volume

00

First Page

1

Last Page

5

Disciplines

Medicine and Health Sciences

Keywords

Computer-aided diagnostics, Lung cancer, Deep learning, CNN-based classification, Batch normalization

Indexed in Scopus

no

Open Access

no

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