Abstract

Computerized segmentation and classification of brain tumors are essential for diagnosis, management, and prediction of the prognosis in neuro-oncology. Standard methodologies for the diagnosis of brain tumors are subjective, based on human experience and qualitative analysis of images, which makes them unsuitable for handling all the variability in tumor types. To address these challenges, this research presents an innovative approach of combining DCNs and PECT images to accurately and quickly segment and classify multi-class brain tumor. The framework uses a DenseNet architecture tailored for high-resolution functional imaging in PECT to yield rich feature representations for tumor identification as well as grading and staging for several types of tumors. The computer algorithm was trained on PECT scans of 250 patients with gliomas, meningiomas and metastatic brain tumors as a training set and test set. Proper segmentation of tumor boundaries, type of tumor, and other important structures were carried out using the multi-class segmentation maps. By implication, the experimental results show that the proposed model has a classification accuracy of 94

Keywords

MSC

68T05, 68U10

Cite this article

@article{cai-7948,
  author  = {J. Hima Bindu and M. Uma Devi},
  title   = {Deep Learning and Single Photon Emission Computed Tomography with Multi-Class Segmentation Maps for Brain Tumor Classifications},
  journal = {Computing and Informatics},
  volume  = {45},
  number  = {2},
  pages   = {439--457},
  year    = {2026},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2026_2_439}
}
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