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This paper presents the development of a novel deep learning model, 3D Trans-DenseUnet++ with Novel Loss Function (3D-TD++-NLF), aimed at improving lung cancer diagnosis through CT image analysis. The model utilizes multi-scale dilated 3D DenseNet with Atrous Spatial Pyramid Pooling (MDDNet-ASPP) for accurate segmentation and classification, achieving an accuracy rate of 92.925%. The research highlights the potential of computer-aided diagnosis systems in enhancing early detection and treatment of lung canc

Author

Anass Roman

Language

EN