CT Imaging-Based Deep Learning System for Non-Small Cell Lung Cancer Detection and Classification

About 85% of all occurrences of lung cancer are classified as Non-Small Cell Lung Cancer (NSCLC), making it a serious worldwide health concern. For better treatment results and patient survival, NSCLC must be detected early and accurately. This research presents an advanced Deep Learning-enabled Lung Cancer Detection and Classification System (LCDCS) aimed at significantly improving diagnostic precision and operational efficiency. Emerging technologies such as artificial intelligence and multi-level convolutional neural networks (ML-CNN) are increasingly being leveraged in CT imaging-based deep learning systems for accurate detection. The outlined framework leverages a multi-layer convolutional neural network to effectively analyse CT scan images and accurately classify lung nodules. Tomek link and Adaptive Synthetic Sampling (ADASYN) are used in a novel way to balance data, address class imbalance, and guarantee strong model performance. Deep learning with a CNN model is utilized to derive features, and the SoftMax function is applied for multi-class classification. Thorough evaluation on datasets like the LUNA16 dataset demonstrates that the system surpasses earlier models and data balancing techniques in accuracy, yielding a training accuracy of 95.8% and a validation accuracy of 96.9%. The findings demonstrate the potential of the suggested method as a trustworthy diagnostic instrument for the prompt identification of lung cancer. The study emphasizes on how crucial it is to combine deep learning architectures with sophisticated data balancing techniques to overcome medical imaging difficulties and raise diagnostic accuracy. Future research attempts to investigate real-time deployment in clinical settings and expand the system's capability to encompass more cancer types.

@article{Rawat2025,
  title     = {CT Imaging-Based Deep Learning System for Non-Small Cell Lung Cancer Detection and Classification},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Devyani Rawat and Sachin Sharma and Shuchi Bhadula},
  doi       = {10.14569/IJACSA.2025.0160561},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160561}
}