Deep Learning based Brain Tumor Identification in MRI Images: A Comparative Analysis of Advanced CNN Architecture

Abstract

The classification of brain tumor is a very important section of medical diagnosis because correct and early diagnosis can greatly enhance the outcome of the patient. To aid in automated and dependable diagnostic methods, this paper examines the efficacy of already trained deep learning model to classify brain magnetic resonance imaging (MRI) scans into four categories, namely, pituitary tumor, meningioma, glioma, and no tumor. This was done using a publicly available set of 7,023 brain MRI images. DenseNet121, ResNet50, Xception and MobileNet four advanced convolutional neural network (CNN) models were also fine-tuned with transfer learning, as well as image preprocessing and data augmentation methods. Transfer learning was used to facilitate effective model adaptation with a low computation complexity and improved classification. According to the experimental findings, DenseNet121 performed better as it obtained the highest classification accuracy of 98.47% and the highest F1 score of 98.47%. The assessed models presented a high level of generalization and steady performance under a variety of evaluation criteria, which also indicates their possible application in the clinical decision support systems. These encouraging findings, however, need additional performance to improve recall on individual tumor classes, as well as the interpretability of deep-learning predictions in clinical settings. On the whole, the results highlight the great potential of the deep learning and transfer learning methods in medical image analysis, which is an opportunity to establish a more reliable, scalable and efficient solution to diagnosis. The next phase of work will involve diversifying the datasets, enhancing the model explainability, and assessing the performance of AI-oriented healthcare systems in practice to make them more widely adopted.