Brain Tumor Detection using Resnet-50 Convolutional Neural Network with Softmax Activation Function

This research highlights that great efforts are being made to improve the accuracy and validity of brain tumor classification using MRI images. Working at multiple resolutions helps to preserve the important features of MRI while reducing irrelevant noise. Below, we briefly discuss the method and its benefits. In this study a non-local means pre-processing technique combining deep learning models ResNet50 and residual connections with CNN allows for better understanding and classification of complex tumor images by utilizing hierarchical and spatial feature extraction. The pre-training and fine-tuning stages allow for better generalization to new unseen material. Fuzzy Clustering Segmentation Method What is interesting about this method is that it combines deep learning methods (CNN and ResNet50) with a classical image processing method, Non-Local Means (NLM). Diagnosing brain tumor and differentiating between tumor types. Fuzzy clustering segmentation method is an effective imaging technique for delineating tumor boundaries. This hybrid approach leverages the best of both worlds to improve tumor detection and classification, aiding in early diagnosis and treatment planning. This is key to identifying the region of interest (tumor), which is often challenging in medical image processing due to the different shapes, sizes, and appearances of tumor. ResNet50-CNN Classification This work aims to address the challenges in brain tumor detection and classification using advanced techniques such as non-local means pre-processing, fuzzy clustering segmentation method, and ResNet50 Convolutional Neural Network (CNN) classification. The reported results show that the proposed method outperforms state-of-the-art methods in various performance metrics such as accuracy, sensitivity, F1 score, specificity, and error rate, indicating that the proposed method is highly effective in terms of accuracy.