International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 18, No. 1, 8 Feb. 2026
Cover page and Table of Contents: PDF (size: 1514KB)
An Explainable AI Framework for Lung Cancer Detection Using Crested Porcupine Optimized Channel-Attention Inceptionresnet
PDF (1514KB), PP.163-179
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Author(s)
Robert R.
1,*
Muneeswaran V.
1
Jose Saji Kumar
2
Index Terms
Lung Cancer, Adaptive Savitzky-Golay Filter, HACR, DS-GLCM, Channel-Attention InceptionResNet, Crested Porcupine Optimization, AI (XAI) Technique
Abstract
Lung cancer is a main reason of death globally, and reducing death rates and enhancing treatment results depend heavily on quick identification. However, medical image diagnosis, including Computed Tomography (CT) scans, is difficult and demands a high level of experience. This research proposes a comprehensive and interpretable Computer-Aided Diagnosis (CAD) structure to identify lung cancer from medical images. The workflow initiates with an Adaptive Savitzky-Golay Filter, effectively enhancing image quality by smoothing while preserving critical structural edges. Hierarchical Adaptive Cluster Refinement (HACR) is then used for precise segmentation, adaptively identifying abnormal lung regions with high accuracy. For feature extraction, the proposed system utilizes the Deep Statistical Gray-Level Co-occurrence Matrix (DS-GLCM) approach, which captures deep spatial and statistical texture features essential for distinguishing cancerous tissue. At last stage, classification is performed using a novel Deep Learning (DL) model Crested Porcupine Optimized (CPO) Channel-Attention (CA) InceptionResNet. The CPO algorithm is exploited to tune the CA- InceptionResNet model's hyperparameters. To ensure transparency and reliability in clinical use, Explainable AI (XAI) technique- Local Interpretable Model-Agnostic Explanations (LIME) is used for visual interpretability, highlighting regions in CT images that contribute the most to model forecasts, thus boosting clinician trust and decision-making. The entire framework is implemented in Python, and experimental results on benchmark lung cancer imaging datasets demonstrate its superior performance in terms of performance metrics with an accuracy of 98.18% with sensitivity of 95.94 % and specificity of 99.10%. The combination of advanced DL and explainable AI makes the proposed framework a promising solution for lung cancer diagnosis.
Cite This Paper
Robert R., Muneeswaran V., Jose Saji Kumar, "An Explainable AI Framework for Lung Cancer Detection Using Crested Porcupine Optimized Channel-Attention Inceptionresnet", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.1, pp.163-179, 2026. DOI:10.5815/ijitcs.2026.01.09
Reference
[1]S. Oh, J. Im, S. R. Kang I. J. Oh, M. S. Kim, “PET-based deep-learning model for predicting prognosis of patients with non-small cell lung cancer,” IEEE Access, no.9, pp.138753-138761, 2021.
[2]S. Matte, S. Mengal, T. Jadhav, P. Jadhav, P. Khawale, A. Khachane, D. G. Takale, “Prostate cancer detection using deep learning and traditional techniques,” IEEE Accessm, no. 9, pp. 27085-27100, 2021.
[3]Y. Chen, Y. Wang, F. Hu, L. Feng, T. Zhou, C. Zheng, “LDNNET: towards robust classification of lung nodule and cancer using lung dense neural network,” IEEE Access, no. 9, pp. 50301-50320, 2021.
[4]R. Robert, V. Muneeswaran, “Current Technology Development in Cancer Detection using Artificial Intelligence: Methodical Exploration and Interpretation,” In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN), pp. 1-7. 2025.
[5]M. Malafaia, F. Silva, I. Neves, T. Pereira, H. P. Oliveira, “Robustness analysis of deep learning-based lung cancer classification using explainable methods,” IEEE access, no.10, pp. 112731-112741, 2022.
[6]X. Fu, L. Bi, A. Kumar, M. Fulham, J. Kim, “An attention-enhanced cross-task network to analyse lung nodule attributes in CT images,” Pattern Recognition, vol.126, pp. 108576, 2022.
[7]D. Riquelme M. A. Akhloufi, “Deep learning for lung cancer nodules detection and classification in CT scans,” AI, vol. 1, no. 1, pp. 28–67, Jan. 2020.
[8]B. Ozdemir, E. Aslan, I. Pacal, “Attention Enhanced InceptionNeXt Based Hybrid Deep Learning Model for Lung Cancer Detection,” IEEE Access, 2025.
[9]E. Dritsas, M. Trigka, “Lung cancer risk prediction with machine learning models,” Big Data and Cognitive Computing, vol. 6, no. 4, pp. 139, 2022.
[10]V. Rajasekar M. P. V aishnnave, S. Premkumar, V. Sarveshwaran, V. Rangaraaj, “Lung cancer disease prediction with CT scan and histopathological images feature analysis using deep learning techniques,” Results in Engineering, no.18, pp. 101111, 2023.
[11]X. Guan, Y. Du, R. Ma, N. Teng, S. Ou, H. Zhao, X. Li, “Construction of the XGBoost model for early lung cancer prediction based on metabolic indices,” BMC medical informatics and decision making, vol. 23, no. 1, pp. 107, 2023.
[12]G. Kasinathan, S. Jayakumar, “Cloud‐Based Lung Tumor Detection and Stage Classification Using Deep Learning Techniques,” Biomed research international, vol. 2022, no. 1, pp. 4185835, 2022.
[13]I. Shafi, S. Din, A. Khan, I. D. L. T. Díez, R. D. J. P. Casanova, K. T. Pifarre, I. Ashraf, “An effective method for lung cancer diagnosis from CT scan using deep learning-based support vector network,” Cancers, vol.14, no. 21, pp. 5457, 2022.
[14]I. Naseer, S. Akram, T. Masood, M. Rashid, A. Jaffar, “Lung cancer classification using modified u-net based lobe segmentation and nodule detection,” IEEE Access, no. 11, pp. 60279-60291, 2023.
[15]W. J. Sori, J. Feng, A. W. Godana, S. Liu, D. J. Gelmecha, “DFD-Net: lung cancer detection from denoised CT scan image using deep learning,” Frontiers of Computer Science, no. 15, pp. 1-13, 2021.
[16]A. A. Shah, H. A. M. Malik, A. Muhammad, A. Alourani, Z. A. Butt, “Deep learning ensemble 2D CNN approach towards the detection of lung cancer,” Scientific reports, vol. 13, no. 1, pp. 2987, 2023.
[17]A. R. Wahab Sait, “Lung cancer detection model using deep learning technique,” Applied Sciences, vol.13, no. 22, pp. 12510, 2023.
[18]A. SU, F. R. PP, A. Abraham, D. Stephen, “Deep learning-based BoVW–CRNN model for lung tumor detection in nano-segmented CT images,” Electronics, vol. 12, no. 1, pp. 14, 2022.
[19]R. Mahum, A. S. Al-Salman, “Lung-RetinaNet: Lung cancer detection using a RetinaNet with multi-scale feature fusion and context module,” IEEE Access, no. 11, pp. 53850-53861, 2023.
[20]M. K. Islam, M. M. Rahman, M. S. Ali, S. M. Mahim, M. S. Miah, “Enhancing lung abnormalities detection and classification using a Deep Convolutional Neural Network and GRU with explainable AI: A promising approach for accurate diagnosis,” Machine Learning with Applications, no. 14, pp. 100492, 2023.
[21]M. Z. Hasan, S. Montaha, I. U. Khan, M. M. Hassan, A. Al Mahmud, A. R. H. Rafid, S. Azam, A. Karim, S.Prountzos, “Fast and efficient lung abnormality identification with explainable AI: A comprehensive framework for chest CT scan and X-ray images,” IEEE Access, no. 12, pp. 31117-31135, 2024.
[22]C. Anam, K. Adi, H. Sutanto, Z. Arifin, W. S. Budi, T. Fujibuchi, G. Dougherty, “Noise reduction in CT images using a selective mean filter,” Journal of biomedical physics & engineering, vol. 10, no. 5, pp. 623, 2020.
[23]S. A. Ştefănigă, “Fine-Tuned Medical Images Denoising using Median Filtering,” Applied Medical Informatics, no. 43, pp. 27-27, 2021.
[24]H. F. Kareem, M. S. AL-Husieny, F. Y. Mohsen, E.A. Khalil, Z. S.Hassan, “Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 21, no. 3, pp. 1731, 2021.
[25]A. Asuntha, A. Srinivasan, “Deep learning for lung Cancer detection and classification. Multimedia Tools and Applications,” pp 1-32, 2020.
[26]S. Shanthi, N Rajkumar, “Lung Cancer Prediction Using Stochastic Diffusion Search (SDS) Based Feature Selection and Machine Learning Methods,” Neural Processing Letters, vol.53, no. 4, 2020
[27]I. Nazir, I. U. Haq, M. M. Khan, M. B. Qureshi, H. Ullah, S. Butt, “Efficient pre-processing and segmentation for lung cancer detection using fused CT images,” Electronics, vol. 11, no. 1, pp. 34, 2021.
[28]D. M. Abdullah, A. M. Abdulazeez, A. B. Sallow, “Lung cancer prediction and classification based on correlation selection method using machine learning techniques,” Qubahan Academic Journal, vol. 1, no. 2, pp. 141-149, 2021.
[29]A. A. Abd Al-Ameer, G. A. Hussien, H. A. Al Ameri, “Lung cancer detection using image processing and deep learning,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 2, pp. 987-993, 2022).