[2604.01264] OkanNet: A Lightweight Deep Learning Architecture for Classification of Brain Tumor from MRI Images

Electrical Engineering and Systems Science > Image and Video Processing arXiv:2604.01264 (eess) [Submitted on 1 Apr 2026] Title:OkanNet: A Lightweight Deep Learning Architecture for Classification of Brain Tumor from MRI Images Authors:Okan Uçar, Murat Kurt View a PDF of the paper titled OkanNet: A Lightweight Deep Learning Architecture for Classification of Brain Tumor from MRI Images, by Okan U\c{c}ar and 1 other authors View PDF HTML (experimental) Abstract:Medical imaging techniques, especially Magnetic Resonance Imaging (MRI), are accepted as the gold standard in the diagnosis and treatment planning of neurological diseases. However, the manual analysis of MRI images is a time-consuming process for radiologists and is prone to human error due to fatigue. In this study, two different Deep Learning approaches were developed and analyzed comparatively for the automatic detection and classification of brain tumors (Glioma, Meningioma, Pituitary, and No Tumor). In the first approach, a custom Convolutional Neural Network (CNN) architecture named "OkanNet", which has a low computational cost and fast training time, was designed from scratch. In the second approach, the Transfer Learning method was applied using the 50-layer ResNet-50 [1] architecture, pre-trained on the ImageNet dataset. In experiments conducted on an extended dataset compiled by Masoud Nickparvar containing a total of $7,023$ MRI images, the Transfer Learning-based ResNet-50 model exhibited superior classi...