Zafar, A.Muneeb, S.Amir, M.Jamil, A.Hameed, A.A.2024-05-192024-05-1920239798350322347https://doi.org/10.1109/AIBThings58340.2023.10291022https://hdl.handle.net/20.500.12713/4345Central Michigan University (CMU);IEEE2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 -- 16 September 2023 through 17 September 2023 -- -- 194014Lung cancer remains a significant global cause of cancer-related deaths, emphasizing the importance of early detection for improving patient survival rates. This paper introduces an enhanced approach that aims to achieve efficient and precise lung tumor detection and segmentation. The proposed method utilizes a multimodal approach by leveraging both CT and PET scans, enabling improved tumor detection. The methodology incorporates state-of-The-Art deep learning architectures, including ResNet, DenseNet, and Inception-v3, for effective tumor classification. Additionally, both immediate fusion (early fusion) and late fusion techniques are applied to integrate data from multiple modalities. The performance of the classification models is evaluated using metrics such as precision, F1 score, accuracy, and sensitivity. The experimental results demonstrate the effectiveness of the proposed approach in accurately segmenting lung tumors. The findings contribute to the existing knowledge in the field of tumor segmentation and medical image analysis, providing valuable insights into the benefits of multimodal fusion and deep learning techniques for lung cancer diagnosis and treatment planning. © 2023 IEEE.eninfo:eu-repo/semantics/closedAccessDeep LearningFeature FusionLung TumorMultimodal Feature ExtractionTumor Detection And SegmentationConference Object2-s2.0-8517850660110.1109/AIBThings58340.2023.10291022N/A