i-manager Publications

An Artificial Intelligence Approach to Lung Cancer Diagnosis using LungNet-TL Model

Quba Jaslin C *, Jerusalin Carol J.**, Lenin Fred A.***

* Department of Artificial Intelligence and Machine Learning, St. Joseph's College of Engineering, Semmancheri, Chennai, Tamilnadu, India.

**-*** Department of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Kanyakumari, Tamilnadu, India.

Periodicity:October - December'2024
DOI : https://doi.org/10.26634/jit.13.4.21514

Abstract

Lung cancer is one of the deadliest types of cancer worldwide, and early diagnosis is critical for improving patient outcomes. This study proposes LungNet-LT, a novel deep learning model specifically designed for detecting and classifying lung cancer using medical imaging data. The LungNet-LT model enhances feature extraction from computed tomography (CT) scans and X-ray images by integrating convolutional neural networks (CNNs) with transfer learning techniques. Compared to conventional diagnostic methods, LungNet-LT demonstrates substantial improvements in classification sensitivity, specificity, and accuracy by leveraging a pre-trained model fine-tuned on lung-specific datasets. Advanced image processing techniques are incorporated to reduce noise and improve cancer cell localization, enabling more reliable predictions for both early-stage and advanced lung cancer cases. The performance of LungNet-LT is validated on publicly available lung cancer datasets and a curated clinical dataset from a partner hospital. Results indicate that LungNet-LT achieves a diagnostic accuracy of over 98.9%, with a significant reduction in false positives and false negatives. These findings highlight the potential of LungNet-LT as a powerful tool to assist physicians in diagnosing lung cancer and improving patient outcomes through prompt and precise actions.

Keywords

Artificial Intelligence, Machine Learning, Medical Imaging, CT Scan, X-Ray, Transfer Learning (TL).

How to Cite this Article?

Jaslin, C. Q., Carol, J. J., and Fred, A. L. (2024). An Artificial Intelligence Approach to Lung Cancer Diagnosis using LungNet-TL Model. i-manager’s Journal on Information Technology, 13(4), 6-17. https://doi.org/10.26634/jit.13.4.21514

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