A Convolutional Neural Network (CNN)- based Approach for Lung Cancer Detection Leveraging Classical Edge Detection Techniques: An AI-based Retrospective Study
Introduction: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnostic imaging, histopathological assessment continues to be the gold standard for accurate classification of lung cancer subtypes. However, manual interpretation of histopathological slides is time-consuming and susceptible to inter-observer variability, underscoring the need for efficient Computer-Aided Diagnostic (CAD) systems. Aim: To develop a Convolutional Neural Network (CNN)- based model for automated classification of lung carcinoma histopathological images by integrating classical gradientbased edge detection techniques to enhance image quality and improve classification performance. Materials and Methods: This retrospective analytical study was conducted in the ICMR-funded laboratory at Brainware University, Kolkata, West Bengal, India. The project commenced in February 2024 and is proposed to conclude in February 2027. To date, it has yielded a dataset of 11,580 histopathological images of lung tissue, categorised as Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LSCC), and Non Malignant (BNM). The present work represents a split study and forms part of a larger ICMR-funded project spanning three years. This component focuses on LUAD, LSCC, and non-malignant lung tissue, thereby encompassing the two most common Non Small Cell Lung Cancer (NSCLC) subtypes (which together account for approximately 85% of lung cancers) along with a normal reference class. Edge enhancement was performed using Sobel and Scharr operators to emphasise structural boundaries. The processed images were used to train a custom-designed CNN architecture comprising three convolutional blocks, each followed by pooling layers, and fully connected layers for classification. The model was trained for 100 epochs with a batch size of 32 and evaluated using accuracy, precision, sensitivity, specificity, and F1-score as performance metrics. Results: Sobel-based preprocessing yielded superior model performance compared with the Scharr method. The proposed CNN trained on Sobel-enhanced images achieved an overall classification accuracy of 93.55%, outperforming the 81.33% accuracy obtained with Scharr-based preprocessing. The Sobelenhanced model also demonstrated stronger generalisation, with AUC values of 0.98, 1.00, and 0.98 for the LUAD, BNM, and LSCC classes, respectively. Conclusion: The integration of edge detection with CNN-based analysis effectively enhances the classification of lung cancer histopathological images. The Sobel operator notably improved model accuracy, precision, and generalisation, suggesting its potential suitability for highlighting morphological boundaries in tissue sections. Future work will extend this framework by exploring additional edge detection methods, implementing stain normalisation, and validating performance on external datasets to further optimise model robustness and clinical applicability.