This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.
Abstract
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, and early detection is essential to improve patient survival. Computed tomography (CT) is currently the reference imaging modality for lung cancer screening. Recent advances in Artificial Intelligence (AI), particularly Deep Learning (DL), have significantly improved automated medical image analysis and diagnostic accuracy. In this study, we propose a Convolutional Neural Network (CNN)-based approach for pulmonary nodule classification. The model was trained using a merged dataset composed of public CT image databases (IQ-OTH/NCCD and SPIE-AAPM) and real-world CT scans collected at Cheikh Zaid Hospital, Morocco. The experimental dataset included 1,103 malignant images, 508 benign images, and 427 normal images. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied to enhance image contrast prior to training. The proposed CNN achieved an accuracy of 99.84%, precision of 99.97%, sensitivity of 99.84%, and specificity of 99.82% for a three-class classification task. These results demonstrate the robustness and clinical potential of the model in assisting radiologists with the classification of indeterminate pulmonary nodules. This work represents an initial translational step toward real-world clinical implementation of AI-based lung nodule classification at Cheikh Zaid Hospital and highlights the feasibility of integrating AI tools into routine radiology workflows.
Lung cancer is the main cause of death related to cancer globally, taking the lives of about 1.8 million people each year. Detecting it early and accurately from CT scan images is very important for better patient results. Three sophisticated Convolutional Neural Network (CNN) models—ResNet50, DenseNet121, and EfficientNetB0—are used in this comprehensive study to automatically classify lung CT scan images into two categories (benign and malignant). The IQ-OTH/NCCD Lung Cancer Dataset, which is openly accessible on Kaggle, was used to train and evaluate the models. This dataset was created by Aditya Mahimkar and contains 1300 CT scan slices from 110 different patient cases. After preprocessing, only benign and malignant CT scan images were used for binary classification. To evaluate the performance of each model following training through transfer learning and fine-tuning, metrics such as accuracy, precision, recall, F1-score, and the confusion matrix were employed. ResNet50 outperformed DenseNet121 and EfficientNetB0 in terms of accuracy. A combined Weighted Ensemble Model that uses the probability outputs from all three networks was also created, which improved the overall classification performance and how well the model works in different situations. This study shows that using deep learning and combining models can be very helpful tools in detecting lung cancer during clinical screenings.
Aryan Shingan, R. Phalnikar· 2026 International Conferenc...· 0 citations
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.
Saurav Mali, Ankur Mukherjee, Biren Parikh et al.· Journal of Clinical and Diag...· 0 citations
Lung cancer remains one of the leading causes of cancer-related mortality worldwide, necessitating early and accurate diagnosis for improved patient survival. This paper proposes a Deep Learning Classification-Based Fast Segmentation (DLC-FS) framework for efficient detection and segmentation of lung cancer from computed tomography (CT) images. The proposed method integrates a lightweight convolutional neural network (CNN) classifier with an optimized U-Net-based segmentation architecture to achieve high accuracy with reduced computational complexity. The framework employs advanced preprocessing techniques, including normalization, noise reduction, and contrast enhancement, followed by feature extraction using deep convolutional layers. A hybrid loss function combining Dice loss and binary cross-entropy is utilized to improve segmentation precision. The model is trained and evaluated on publicly available lung cancer datasets, achieving a classification accuracy of 97.8%, precision of 96.5%, recall of 95.9%, and an F1-score of 96.2%. For segmentation performance, the proposed approach attains a Dice Similarity Coefficient (DSC) of 94.7%, Intersection over Union (IoU) of 92.3%, and reduces inference time by 28% compared to conventional U-Net models. Experimental results demonstrate that the proposed DLC-FS framework significantly outperforms existing state-of-the-art methods in both detection accuracy and computational efficiency. The fast segmentation capability makes the system suitable for real-time clinical applications, assisting radiologists in early diagnosis and treatment planning. Future work will focus on multi-modal data integration and deployment in edge-based healthcare systems.
R. Gnanakumari, U. R, A. S et al.· International journal of com...· 0 citations
One of the leading causes of cancer-related mortality worldwide is lung cancer. Improving patient survival requires early and precise diagnosis. Recent developments in deep learning make it possible to analyse histopathological lung pictures automatically. Because most deep learning models are opaque and considered black-box systems, their application in clinical practice is constrained. An explainable deep-learning framework for the categorisation of lung cancer is presented in this article. Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation and a custom Convolutional Neural Network are also used. Three classes of histopathological lung tissue images: lung squamous cell carcinoma, lung adenocarcinoma, and normal tissue. Images are scaled to 224*224 pixels and preprocessed using data augmentation and normalisation techniques to enhance generalisation before training. With a test accuracy of 96.62%, the suggested Custom CNN is comparable to existing deep learning models like DenseNet121 (96.6%) and other CNN-based methods documented in earlier research. The suggested approach uses Grad-CAM to provide visual interpretability alongside classification performance.
Prathamesh Patil, Pradnya.S.Kulkarni· 2026 4th International Confe...· 0 citations
Lung cancer is one of the deadliest malignancies, known for rapid growth and has high potential of spreading the original tumor cells to other cells of the body. According to GLOBOCAN 2020, there were 2.2 million new lung cancer cases and 1.8 million deaths, making up 18% of global cancer deaths. The American Lung Association (ALA) reports that only 25.8% of cases are detected early, with a 5-year survival rate. Early detection is crucial but remains difficult due to nonspecific symptoms and current imaging limitations. This study proposes a deep Convolutional Neural Network (CNN) approach to improve lung cancer detection by facilitating accurate, automated diagnosis of lung nodules in early stage cancer. We combine two advanced deep learning models, VGG16 and U-Net++, to enhance the classification of medical images. The approach leverages VGG16’s strengths in feature extraction and U-Net++’s robust multiscale processing to handle the diverse shapes and sizes of tumors in lung tissue. These models are ensembled to advance our capabilities in processing medical images. Features extracted from both models are concatenated to create a comprehensive and flexible representation of the input data. The feature set is flattened and then undergoes further processing using Dense layers, Batch Normalization, and Dropout layers to improve generalization and prevent overfitting. The final output layer classifies the images as ‘Benign,’ ‘Malignant,’ or ‘Normal.’ Extensive experiments on a lung image dataset demonstrate significant classification improvements over individual models. Performance evaluation using metrics such as accuracy, precision, recall, and F1-score shows higher results for the ensemble model. This study concludes that integrating VGG16 and U-Net + + into this CNN architecture can significantly enhance lung cancer detection performance by achieving a remarkable accuracy of 96% and provide a reliable tool for clinicians in early stage diagnosis and treatment monitoring. Our proposed brings improvement in accuracy, segmentation and feature extraction and thereby proves to be one among the top models available.
Sanjeevkumar B., Varun S. P., S. Babu et al.· Scientific Reports· 0 citations
The proposed system demonstrates potential as an assistive tool for automated lung cancer screening, warranting further validation on larger and multi-institutional datasets before clinical application.
Vishwas V. Patange, Jagadish B. Jadhav, Sanjay L. Nalbalwar et al.· Scientific Reports· 0 citations
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