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.

翻译：医学成像技术，尤其是磁共振成像（MRI），被公认为神经系统疾病诊断和治疗规划的黄金标准。然而，MRI图像的人工分析对放射科医生而言耗时费力，且易因疲劳导致人为误差。本研究开发了两种不同的深度学习方法，并对其在脑肿瘤（胶质瘤、脑膜瘤、垂体瘤及无肿瘤）自动检测与分类中的表现进行了比较分析。第一种方法从头设计了名为"OkanNet"的自定义卷积神经网络（CNN）架构，其具有低计算成本和快速训练时间的特性。第二种方法采用基于ImageNet数据集预训练的50层ResNet-50 [1]架构进行迁移学习。在Masoud Nickparvar编制的包含总共7023张MRI图像的扩展数据集实验中，基于迁移学习的ResNet-50模型展现出卓越的分类性能，达到了96.49%的准确率和0.963的精确率。相比之下，自定义OkanNet架构的准确率为88.10%，但其训练时间约为311秒，比ResNet-50快约3.2倍，从而证明其作为计算能力有限的移动和嵌入式系统的强力替代方案。本研究通过实验数据揭示了医学图像分析中模型深度与计算效率之间的权衡关系。