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

2026-04-01

AI Summary

This study addresses the time-consuming and error-prone nature of manual analysis of brain tumor MRI images by proposing OkanNet, a lightweight convolutional neural network (CNN) architecture for the automated classification of four categories: glioma, meningioma, pituitary tumor, and no tumor. Compared to a ResNet-50–based transfer learning approach, OkanNet achieves a competitive accuracy of 88.10% while reducing computational overhead significantly—training 3.2 times faster—whereas ResNet-50 attains a higher accuracy of 96.49% at substantially greater resource cost. This work effectively balances model efficiency and diagnostic accuracy, offering a practical solution for medical image analysis in resource-constrained settings.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Medical and Biological Imaging
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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 classification performance, achieving $96.49\%$ Accuracy and $0.963$ Precision. In contrast, the custom OkanNet architecture reached an accuracy rate of $88.10\%$; however, it proved to be a strong alternative for mobile and embedded systems with limited computational power by yielding results approximately $3.2$ times faster ($311$ seconds) than ResNet-50 in terms of training time. This study demonstrates the trade-off between model depth and computational efficiency in medical image analysis through experimental data.

Problem

• brain tumor classification
• MRI images
• medical image analysis
• automatic diagnosis
• neurological diseases

Innovation

• OkanNet
• lightweight CNN
• brain tumor classification
• computational efficiency
• medical image analysis