International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Automated Lung Tumor Classification and Characterization Using Multi-Task Deep Learning |
|---|---|
| ABSTRACT | Early stage identification is crucial for improving patient outcomes because lung cancer is one of the leading causes of high death rates globally. A deep learning-based method for automatically segmenting lung tumors from computed tomography (CT) images is presented in this work. Following routine preprocessing and data augmentation, a U-Net architecture was trained using the LIDC-IDRI dataset and the Kaggle rasoulisaeid/lung-cancer-segment dataset. A Streamlit application is used to install the final model (lung-cancer-segmenter.pth), which enables users to upload CT scans and see segmented tumor locations in real time. A Dice coefficient of 0.0966, IoU of 0.0507, accuracy of 0.9925, and sensitivity of 0.7269 were obtained from evaluation on 3,154 test samples. Due to class imbalance and the tiny size of tumor areas, the model exhibits great sensitivity but low precision. The method serves as a useful computer-aided tumor localization tool, and future development will concentrate on increasing segmentation accuracy and incorporating stage predication. The novelty of this work lies in the development of an end -to – end and lightweight lung tumor segmentation framework that integrates preprocessing, U-net based segmentation, and real-time visualization within a single Streamlit based application. Unlike conventional studies that only focus solely on segmentation accuracy and precision, the proposed approach emphasizes practically usability by enabling the interactive and responsive lung tumor visualization and classification that provides a foundation for the stage prediction, thereby supporting early clinical assessment. |
| AUTHOR | K. ANURANJANI, MAMBALAM VENKATA SAI KOUSHIK, P. MOULI GANGADHAR, P. KRISHNA NAGA CHAITANYA, MARISA SRINIVAS Department of Computer Science and Engineering, Bharath Institute of Higher Education and Research, Tambaram, Chennai, Tamil Nadu, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405029 |
| pdf/29_Automated Lung Tumor Classification and Characterization Using Multi-Task Deep Learning.pdf | |
| KEYWORDS | |
| References | [1] S. Armato III, G. McLennan, L. Bidaut et al., “The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans,” Medical Physics, vol. 38, no. 2, pp. 915–931, 2011. [2] R. Saeid, “Lung Cancer Segmentation Dataset,” Kaggle, 2021. [Online].Available:https://www.kaggle.com/datasets/rasoulisaeid/lung- cancer-segment. [3] O. Ronneberger, P. Fischer and T. Brox, “U-Net: Convolutional Net- works for Biomedical Image Segmentation”, in Proc.MICCAI, 2015, pp. 234–241. [4] O. Oktay et al., “Attention U-Net: Learning Where to Look for the Pancreas,” arXiv preprint arXiv:1804.03999, 2018. [5] Z. Zhou, M. Siddiquee, N. Tajbakhsh and J. Liang, “UNet++: A Nested U-Net Architecture for Medical Image Segmentation,” in Deep Learning in Medical Image Analysis, Springer, 2018. [6] O¨ .C¸ ic¸ ek, A. Abdulkadir, S. Lienkamp, T. Brox and O. Ronneberger, “3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation,” in Proc. MICCAI, 2016, pp. 424–432. [7] S. Setio et al., “Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in CT Images: The LUNA16 Challenge,” IEEE Transactions on Medical Imaging, vol. 37, no. 4, pp. 1103–1115, 2018. [8] W. Shen et al., “Multi-Scale Convolutional Neural Networks for Lung Nodule Classification,” in Proc. MICCAI, 2015, pp. 588–599. [9] F. Milletari, N. Navab and S. Ahmadi, “V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation,” in Proc. 3DV, 2016, pp. 565–571. [10] H. Chen et al., “RA-UNet: A Hybrid Deep Attention-Aware Network for Liver and Lung Tumor Segmentation,” Medical Image Analysis, vol. 59, 2020. [11] J. Cheng et al., “Computer-Aided Detection and Diagnosis of Lung Cancer Using Radiomics and Deep Learning Methods,” IEEE Access, vol. 8, pp. 117897–117906, 2020. [12] R. Javed, T. Abbas, A. H. Khan et al., “Deep learning for lung cancer detection: A review,” Artificial Intelligence Review, vol. 57, Art. no. 197, 2024, doi: 10.1007/s10462-024-10807-1. [13] S. L. Tan, G. Selvachandran, R. Paramesran et al., “Lung cancer detection systems applied to medical images: A state-of-the-art survey,” Archives of Computational Methods in Engineering, vol. 32, pp. 343–380, 2025, doi: 10.1007/s11831-024-10141-3. [14] K. Abdullahi, K. Ramakrishnan, and A. B. Ali, “Deep learning techniques for lung cancer diagnosis with computed tomography imaging: A systematic review for detection, segmentation, and classification,” Information, vol. 16, no. 6, Art. no. 451, 2025, doi: 10.3390/info16060451. [15] A. Bagheri Tofighi, A. Ahmadi, and H. Mosadegh, “Improving lung cancer detection via MobileNetV2 and stacked-GRU with explainable AI,” International Journal of Information Technology, vol. 17, pp. 1189–1196, 2025. [16] V. Juliet Rani and K. K. Thanammal, “Lung cancer segmentation using MIBFS clustering and energetic BPN,” International Journal of Information Technology, vol. 15, no. 2, pp. 905–916, 2023. |
