Image Data Driven Lung Cancer Classification using Deep Learning and Optimization Algorithm

Title

Image Data Driven Lung Cancer Classification using Deep Learning and Optimization Algorithm

Creator

Samal, Sonali; Champatiray, Chiranjibi

Description

Lung cancer continues to be a major contributor to global cancer mortality, underscoring the importance of early detection and accurate diagnosis. This work introduces an integrated framework that leverages deep learning in combination with Bayesian optimization to achieve robust lung cancer classification. Convolutional Neural Networks (CNNs) are employed for feature extraction and image analysis, while Bayesian optimization is applied to automatically fine-tune critical hyperparameters, thereby improving accuracy and minimizing training overhead. The methodology focuses on the analysis of computed tomography (CT) images to distinguish between different lung cancer categories. By addressing the limitations of manual hyperparameter selection, the proposed framework enhances the efficiency and reliability of deep learning models in medical imaging. The outcomes of this study highlight its potential contribution to computer-aided diagnosis, offering clinicians an effective decision-support tool for precise and timely lung cancer detection. 2025 IEEE.

Source

1st IEEE International Conference on Data Science and Intelligent Network Computing, ICDSINC 2025;pp.53-57

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Batch Size Selection; CNN; Hyperparameter Tuning; Learning Rate Optimization

Coverage

Samal S., Department of Computer Science and Engineering, Alliance School of Advanced Computing, Alliance University, Bengaluru, India; Champatiray C., Department of Mechanical and Automobile Engineering, School of Engineering and Technology, Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833156524-4;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICDSINC66221.2025.11448185

https://www.scopus.com/pages/publications/105037444824?origin=resultslist