Title: Lung disease classification using deep learning 1-D convolutional neural network

Authors: J. Viji Gripsy; T. Divya

Addresses: PSGR Krishnammal College for Women, Peelamedu, Coimbatore, Tamil Nadu, India ' PSGR Krishnammal College for Women, Peelamedu, Coimbatore, Tamil Nadu, India

Abstract: Healthcare plays a crucial role in human life, particularly in the early diagnosis of diseases such as lung cancer, which affects people worldwide. Early detection of lung cancer can significantly improve treatment outcomes. This paper proposes a 1-D CNN deep learning architecture to classify patients into low, medium, and high-risk categories for lung cancer. The model achieves 97% training accuracy and 96.33% test accuracy, outperforming existing classification algorithms in accuracy, precision, recall, F1-score, and AUC. These results highlight the effectiveness of the proposed architecture in the early diagnosis of lung cancer.

Keywords: lung disease; classification; 1-D convolutional neural network; 1-D CNN; prediction.

DOI: 10.1504/IJDMMM.2025.150986

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.4, pp.433 - 449

Received: 28 Mar 2024
Accepted: 24 Aug 2024
Published online: 07 Jan 2026 *

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