Application of deep neural networks with transfer learning in the detection of tumor brain cells

This thesis examines the effectiveness and applicability of deep learning models in medical image analysis. Specifically, it focuses on the classification of magnetic resonance images (MRI) containing tumors of various types. In the introduction section, the importance of medical image analysis, the rise of artificial intelligence and deep leaning in this field, and the objective of this study are discussed in detail. In the thesis, four popular deep learning models, namely ResNet, MobileNet, DenseNet, and InceptionV4, are utilized. Each model is trained using the transfer learning method with ImageNet weights and trained from scratch without ImageNet weights. The performance of the models is evaluated based on the accuracy of classifying various types of tumors. Although the results are generally evalueted across all metrics, the final evaluation is done based on the F1-score due to the imbalanced distribution of the classes. It is observed that the InceptionV4 model has the highest overall success rate (%96 F1-score) when trained with ImageNet weights. While other models also exhibit comparably high accuracy rates, they face challenges in classifying certain types of tumors. Notably, the performance of all models dropped in certain classes such as Granuloma T2. Additionally, the thesis emphasizes that transfer learning aids in the efficient and effective training of models. Moreover, it is noted that conducting model training for specific datasets and adjusting hyperparameters could enhance the performance of the models. Ultimately, the thesis provides valuable insights into which deep learning model or models are most suitable for tumor classification in clinical applications, while understanding the limitations and challenges of these models can guide future research and developments. This study highlights the importance of deep learning in medical image analysis and contributes to the field by paving the way for more accurate diagnoses and effective treatments for patients.