Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Conventional computed tomography (CT) imaging, while essential for detection and staging, has limitations in distinguishing benign from malignant lesions and providing interpretable diagnostic insights. To address this challenge, this study proposes a dual-modal artificial intelligence framework that integrates CT radiology with hematoxylin and eosin (H&E) histopathology for lung cancer diagnosis and subtype classification. The system employs convolutional neural networks to extract radiologic and histopathologic features and incorporates clinical metadata to improve robustness. Predictions from both moda...
| Authors | Baramee Sukumal, Aueaphum Aueawatthanaphisut |
|---|---|
| Primary category | eess.IV |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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