Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis
Aim: Lung cancer is a leading cause of cancer-related deaths globally, where early and accurate diagnosis significantly improves survival rates. This study proposes an AI-based diagnostic framework integrating U-Net for lung nodule segmentation and a custom convolutional neural network (CNN) for bin...
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Open Exploration Publishing Inc.
2025-07-01
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| Series: | Exploration of Medicine |
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| Online Access: | https://www.explorationpub.com/uploads/Article/A1001341/1001341.pdf |
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| author | Ashwin Kumar Azhagarasan Prashanthi Bhaskaran Arunkumar Ramachandran Kalpana Sivalingam |
| author_facet | Ashwin Kumar Azhagarasan Prashanthi Bhaskaran Arunkumar Ramachandran Kalpana Sivalingam |
| author_sort | Ashwin Kumar Azhagarasan |
| collection | DOAJ |
| description | Aim: Lung cancer is a leading cause of cancer-related deaths globally, where early and accurate diagnosis significantly improves survival rates. This study proposes an AI-based diagnostic framework integrating U-Net for lung nodule segmentation and a custom convolutional neural network (CNN) for binary classification of nodules as benign or malignant. Methods: The model was developed using the Barnard Institute of Radiology (BIR) Lung CT dataset. U-Net was used for segmentation, and a custom CNN, compared with EfficientNet B0, VGG-16, and Inception v3, was implemented for classification. Due to limited subtype labels and diagnostically ambiguous “suspicious” cases, classification was restricted to a binary task. These uncertain cases were reserved for validation. Overfitting was addressed through stratified 5-fold cross-validation, dropout, early stopping, L2 regularization, and data augmentation. Results: EfficientNet B0 achieved ~99.3% training and ~97% validation accuracy. Cross-validation yielded consistent metrics (accuracy: 0.983 ± 0.014; F1-score: 0.983 ± 0.006; AUC = 0.990), confirming robustness. External validation on the LIDC-IDRI dataset demonstrated generalizability across diverse populations. Conclusions: The proposed AI model shows strong potential for clinical deployment in lung cancer diagnosis. Future work will address demographic bias, expand multi-center data inclusion, and explore regulatory pathways for real-world integration. |
| format | Article |
| id | doaj-art-fd122e88c3c84e9e9ff8a1373561110a |
| institution | DOAJ |
| issn | 2692-3106 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Open Exploration Publishing Inc. |
| record_format | Article |
| series | Exploration of Medicine |
| spelling | doaj-art-fd122e88c3c84e9e9ff8a1373561110a2025-08-20T02:43:13ZengOpen Exploration Publishing Inc.Exploration of Medicine2692-31062025-07-016100134110.37349/emed.2025.1001341Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosisAshwin Kumar Azhagarasan0https://orcid.org/0000-0001-5640-2656Prashanthi Bhaskaran1https://orcid.org/0009-0006-5827-1649Arunkumar Ramachandran2https://orcid.org/0000-0002-6665-5312Kalpana Sivalingam3https://orcid.org/0000-0003-3825-3145Current address: Radiodiagnosis, Sree Balaji Medical College and Hospital, Chennai 600003, Tamil Nadu, India; Radiodiagnosis, Bernad Institute of Radiodiagnosis, Madras Medical College, Chennai 600003, Tamil Nadu, IndiaDepartment of Computer Science, St. Peter’s Institute of Higher Education and Research (Deemed to be University), Chennai 600054, Tamil Nadu, IndiaMultidisciplinary Research Unit (MRU), Department of Health Research, Madras Medical College, Chennai 600003, Tamil Nadu, IndiaBarnard Institute of Radiology, Madras Medical College, Chennai 600003, Tamil Nadu, IndiaAim: Lung cancer is a leading cause of cancer-related deaths globally, where early and accurate diagnosis significantly improves survival rates. This study proposes an AI-based diagnostic framework integrating U-Net for lung nodule segmentation and a custom convolutional neural network (CNN) for binary classification of nodules as benign or malignant. Methods: The model was developed using the Barnard Institute of Radiology (BIR) Lung CT dataset. U-Net was used for segmentation, and a custom CNN, compared with EfficientNet B0, VGG-16, and Inception v3, was implemented for classification. Due to limited subtype labels and diagnostically ambiguous “suspicious” cases, classification was restricted to a binary task. These uncertain cases were reserved for validation. Overfitting was addressed through stratified 5-fold cross-validation, dropout, early stopping, L2 regularization, and data augmentation. Results: EfficientNet B0 achieved ~99.3% training and ~97% validation accuracy. Cross-validation yielded consistent metrics (accuracy: 0.983 ± 0.014; F1-score: 0.983 ± 0.006; AUC = 0.990), confirming robustness. External validation on the LIDC-IDRI dataset demonstrated generalizability across diverse populations. Conclusions: The proposed AI model shows strong potential for clinical deployment in lung cancer diagnosis. Future work will address demographic bias, expand multi-center data inclusion, and explore regulatory pathways for real-world integration.https://www.explorationpub.com/uploads/Article/A1001341/1001341.pdfai in healthcarenodule segmentationdiagnostic precisionlung cancer classificationconvolutional neural network |
| spellingShingle | Ashwin Kumar Azhagarasan Prashanthi Bhaskaran Arunkumar Ramachandran Kalpana Sivalingam Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis Exploration of Medicine ai in healthcare nodule segmentation diagnostic precision lung cancer classification convolutional neural network |
| title | Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis |
| title_full | Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis |
| title_fullStr | Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis |
| title_full_unstemmed | Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis |
| title_short | Artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network—from imaging to diagnosis |
| title_sort | artificial intelligence framework for lung cancer nodule segmentation and classification using convolutional neural network from imaging to diagnosis |
| topic | ai in healthcare nodule segmentation diagnostic precision lung cancer classification convolutional neural network |
| url | https://www.explorationpub.com/uploads/Article/A1001341/1001341.pdf |
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