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Lung cancer is a leading cause of cancer-related problems worldwide, and its early diagnosis is helpful for improving patient results. This paper provides an overview of an innovative approach to lung cancer diagnosis using Artificial Intelligence (AI). The proposed system integrates AI technologies, such as deep learning and medical image analysis, to enhance the accuracy and efficiency of lung cancer detection. It focuses on the detection of lung cancer through a multi-stage approach. Initially, the widely used VGG-16 and VGG-19 convolutional neural network architectures are employed to establish a baseline for lung cancer detection. Performance metrics such as accuracy, precision, recall, and F1 score are evaluated to gauge the effectiveness of these pre-trained models. Subsequently, a novel modified convolutional neural network architecture is developed to enhance the accuracy and reduce false positives and false negatives in lung cancer detection. The modified architecture takes advantage of the unique characteristics of lung cancer imagery, incorporating features that exploit subtle patterns and anomalies often associated with this disease. A comparative analysis between the VGG models and the custom architecture is conducted, allowing for the identification of areas for improvement. The goal is to advance the accuracy and effectiveness of lung cancer detection, potentially contributing to early diagnosis and improved patient outcomes in the field of medical imaging and diagnostics. It is found that Modified CNN model produces more accuracy of 93.4% and the model is performing it’s best in all the other performance metrics too.
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Copyright © 2025, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Saranya, drsaranya6@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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