Open Access

ARTICLE

An Enhanced Lung Cancer Detection Approach Using Dual-Model Deep Learning Technique

by Sumaia Mohamed Elhassan¹, Saad Mohamed Darwish^1,*, Saleh Mesbah Elkaffas²

1 Department of Information Technology, Institute of Graduate Studies and Research, Alexandria University, Alexandria, 21526, Egypt
2 College of Computing and Information Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria, P.O. Box 1029, Egypt

* Corresponding Author: Saad Mohamed Darwish. Email:

(This article belongs to the Special Issue: Advances in AI-Driven Computational Modeling for Image Processing)

Computer Modeling in Engineering & Sciences 2025, 142(1), 835-867. https://doi.org/10.32604/cmes.2024.058770

Received 20 September 2024; Accepted 15 November 2024; Issue published 17 December 2024

Abstract

Lung cancer continues to be a leading cause of cancer-related deaths worldwide, emphasizing the critical need for improved diagnostic techniques. Early detection of lung tumors significantly increases the chances of successful treatment and survival. However, current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue. Single-model deep learning technologies for lung cancer detection, while beneficial, cannot capture the full range of features present in medical imaging data, leading to incomplete or inaccurate detection. Furthermore, it may not be robust enough to handle the wide variability in medical images due to different imaging conditions, patient anatomy, and tumor characteristics. To overcome these disadvantages, dual-model or multi-model approaches can be employed. This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models: a Convolutional Neural Network (CNN) for categorization and the You Only Look Once (YOLOv8) architecture for real-time identification and pinpointing of tumors. CNNs automatically learn to extract hierarchical features from raw image data, capturing patterns such as edges, textures, and complex structures that are crucial for identifying lung cancer. YOLOv8 incorporates multi-scale feature extraction, enabling the detection of tumors of varying sizes and scales within a single image. This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect. Furthermore, through the utilization of cutting-edge data augmentation methods, such as Deep Convolutional Generative Adversarial Networks (DCGAN), the suggested approach can handle the issue of limited data and boost the models’ ability to learn from diverse and comprehensive datasets. The combined method not only improved accuracy and localization but also ensured efficient real-time processing, which is crucial for practical clinical applications. The CNN achieved an accuracy of 97.67% in classifying lung tissues into healthy and cancerous categories. The YOLOv8 model achieved an Intersection over Union (IoU) score of 0.85 for tumor localization, reflecting high precision in detecting and marking tumor boundaries within the images. Finally, the incorporation of synthetic images generated by DCGAN led to a 10% improvement in both the CNN classification accuracy and YOLOv8 detection performance.

---

Keywords

---