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

Okan Uçar, Murat Kurt

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.

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

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 MRI images, the Transfer Learning-based ResNet-50 model exhibited superior classification performance, achieving Accuracy and Precision. In contrast, the custom OkanNet architecture reached an accuracy rate of ; however, it proved to be a strong alternative for mobile and embedded systems with limited computational power by yielding results approximately times faster ( 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.

Paper Structure

This paper contains 17 sections, 5 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Applied Algorithms and Methods
  3. Dataset and Preprocessing
  4. Method 1: Custom CNN Architecture (OkanNet)
  5. Convolution and Activation Layers
  6. Pooling & Dropout
  7. Method 2: Transfer Learning of ResNet-50
  8. Experimental Setup and Evaluation Metrics
  9. Hyperparameters
  10. Performance Metrics
  11. Evaluation of Obtained Data
  12. Training Process and Convergence Analysis
  13. Numerical Performance Comparison
  14. Confusion Matrix Analysis
  15. Visual Verification and Computational Cost
  16. ...and 2 more sections

Figures (3)

  • Figure 1: Training Processes of Models. The blue line (OkanNet) represents learning from scratch, while the red dashed line (ResNet-50) represents the adaptation of transferred knowledge.
  • Figure 2: Confusion Matrices. Left: OkanNet, Right: ResNet-50.
  • Figure 3: Sample Predictions Performed with ResNet-50 Model (Green: Correct).