2026-10-27T23:58:42Zhttps://kostu-rdlopen.rawdatalibrary.net/oai/request

oai:kostu-rdlopen.rawdatalibrary.net:kostu/102162026-10-27T23:53:12Zopenairecom_kostu_10000col_kostu_10001com_kostu_10007col_kostu_10060

Brain Tissue Classification from Hyperspectral Images Using Convolutional Neural NetworksKaraduman, IsmetPehlivanoglu, Meltem KurtEker, Ayse GulDuru, NevcihanDundar, Tolga TuranSerdar Mutluer, AhmetAdali, E.artificial intelligencebrain tissueclassificationdeep learninghyperspectral imagingMany of the decisions made and taken in the field of health are important for human life to be sufficient. In such an important area where human transactions are concerned, artificial intelligence-supported studies have a very important place, especially in recent years. This is the correct classification of four classes (Normal Tissue, Tumor Tissue, Hypervascularized Tissue and Background) separately with the 3D-CNN based deep learning model on the In-vivo Hyperspectral Human Brain Image dataset. For this purpose, firstly, three different data settings with 128 spectral bands were obtained from raw images, each containing 826 spectral bands, with dimension reduction methods. In this way, each data setting was made with the designs used in the 3D-CNN based deep learning model. As a result of the experimental studies, the highest F1 score of 99.69% was achieved with the 3D-CNN-based deep learning model.Institute of Electrical and Electronics Engineers Inc.2025Conference Objecthttps://rdlopen.rawdatalibrary.net/r/kostu/10216https://doi.org/10.1109/ubmk67458.2025.112069472521-1641enInternational Conference on Computer Science and Engineering, UBMKinfo:eu-repo/semantics/closedAccessinfo:eu-repo/semantics/conferenceObjecthttps://kostu-rdlopen.rawdatalibrary.net/tr/items/kostu/10216