A Multi-modal Approach to Lung Tumor Detection using Deep Learning

Basit Öğe Kaydı
dc.contributor.authorZafar, A.
dc.contributor.authorMuneeb, S.
dc.contributor.authorAmir, M.
dc.contributor.authorJamil, A.
dc.contributor.authorHameed, A.A.
dc.date.accessioned2024-05-19T14:33:49Z
dc.date.available2024-05-19T14:33:49Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.descriptionCentral Michigan University (CMU);IEEEen_US
dc.description2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 -- 16 September 2023 through 17 September 2023 -- -- 194014en_US
dc.description.abstractLung 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.en_US
dc.identifier.doi10.1109/AIBThings58340.2023.10291022
dc.identifier.isbn9798350322347
dc.identifier.scopus2-s2.0-85178506601en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1109/AIBThings58340.2023.10291022
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4345
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDeep Learningen_US
dc.subjectFeature Fusionen_US
dc.subjectLung Tumoren_US
dc.subjectMultimodal Feature Extractionen_US
dc.subjectTumor Detection And Segmentationen_US
dc.titleA Multi-modal Approach to Lung Tumor Detection using Deep Learningen_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

Scopus İndeksli Yayınlar Koleksiyonu