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International Journal of Zoology and Applied Biosciences Research Article
AI-driven neural network models for lung cancer detection and tumor localization
Vijai Krishna V, Kaaviya A, Sounthararasu V, Florence A and Chandra Lekha S B
Year : 2025 | Pages: 150-154
Received on: 19/09/2025
Revised on: 20/10/2025
Accepted on: 28/10/2025
Published on: 15/11/2025
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Vijai Krishna V, Kaaviya A, Sounthararasu V, Florence A and Chandra Lekha S B ( 2025).
AI-driven neural network models for lung cancer detection and tumor localization
. International Journal of Zoology and Applied Biosciences, 10( 6), 150-154.
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Abstract
Lung cancer remains one of the leading causes of cancer mortality worldwide, primarily due to delayed diagnosis and challenges in accurately identifying malignant lesions at an early stage. Recent advancements in artificial intelligence (AI), particularly neural networks, have shown remarkable potential in enhancing diagnostic precision through automated image analysis. This study presents an AI-driven framework employing deep neural network architectures for efficient detection and precise localization of lung tumors using computed tomography (CT) imaging data. The proposed model integrates convolutional neural networks (CNNs) for feature extraction, followed by region-based and segmentation-oriented modules to improve tumor boundary identification. Experimental evaluation using benchmark datasets demonstrates substantial improvements in sensitivity, specificity, and localization accuracy compared with traditional machine learning and radiologist-dependent methods. The findings highlight the capability of neural networks to support early diagnosis, reduce human error, and streamline clinical workflows, suggesting their potential for real-world integration in computer-aided diagnosis (CAD) systems.
Keywords
Lung cancer detection, Tumor localization, Deep neural networks, Convolutional neural networks.
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© The Author(s) 2025. This article is published by International Journal of Zoology and Applied Biosciences under the terms of the Creative Commons Attribution 4.0 International License (creativecommons.org), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
