ArticleDiscover oncology2025
Multi-objective deep learning for lung cancer detection in CT images: enhancements in tumor classification, localization, and diagnostic efficiency.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Computer vision augmented indocyanine green near-infrared fluorescence (ICG-NIRF) signal-to-background ratio (SBR) for identification of extrahepatic biliary structures in laparoscopic cholecystectomy.Surgical endoscopy · 2026Article
- A Systematic Literature Review of You Only Look Once Architectures (v1-v12) in Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Review
- Dual-database Bibliometric Analysis Combined with Gephi-based Network Visualization of Artificial Intelligence Applications in the Identification and Diagnosis of Thyroid Space-occupying Lesions.Current medical imaging · 2026Article
- Detection and classification of lung cancer using sequential hybridization of CNN and RNN type architectures.Frontiers in big data · 2026Article
- Three-dimensional reconstruction of lung tumors from computed tomography scans using adversarial and transductive learning.Scientific reports · 2025Article
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13 authors.
Funding
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Abstract
objectiveThis study aims to develop and evaluate an advanced deep learning framework for the detection, classification, and localization of lung tumors in computed tomography (CT) scan images. MATERIALS AND
methodsThe research utilized a dataset of 1608 CT scan images, including 623 cancerous and 985 non-cancerous cases, all carefully labeled for accurate tumor detection, classification (benign or malignant), and localization. The preprocessing involved optimizing window settings, adjusting slice thickness, and applying advanced data augmentation techniques to enhance the model's robustness and generalizability. The proposed model incorporated innovative components such as transformer-based attention layers, adaptive anchor-free mechanisms, and an improved feature pyramid network. These features enabled the model to efficiently handle detection, classification, and localization tasks. The dataset was split into 70% for training, 15% for validation, and 15% for testing. A multi-task loss function was used to balance the three objectives and optimize the model's performance. Evaluation metrics included mean average precision (mAP), intersection over union (IoU), accuracy, precision, and recall.
resultsThe proposed model demonstrated outstanding performance, achieving a mAP of 96.26%, IoU of 95.76%, precision of 98.11%, and recall of 98.83% on the test dataset. It outperformed existing models, including You Only Look Once (YOLO)v9 and YOLOv10, with YOLOv10 achieving a mAP of 95.23% and YOLOv9 achieving 95.70%. The proposed model showed faster convergence, better stability, and superior detection capabilities, particularly in localizing smaller tumors. Its multi-task learning framework significantly improved diagnostic accuracy and operational efficiency.
conclusionThe proposed model offers a robust and scalable solution for lung cancer detection, providing real-time inference, multi-task learning, and high accuracy. It holds significant potential for clinical integration to improve diagnostic outcomes and patient care.
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