Paper Title
MICROSCOPIC BRAIN TUMOR DETECTION AND CLASSIFICATION USING 3D CNN AND FEATURE SELECTION ARCHITECTURE

Abstract
Brain tumor detection is a crucial aspect of healthcare, and deep learning techniques have shown significant potential in this domain. using Kaggle dataset, a model achieved 99.8% accuracy in brain tumor classification. The study employed a hybrid feature extraction approach with regularized extreme learning machine to classify brain tumors into three types: glioma, meningioma, and pituitary gland tumors, along with healthy brains. Deep learning models, such as CNN, have been successful in extracting hierarchical features from complex visual data, leading to powerful and highly accurate models. Transfer learning, which involves fine-tuning pre-trained models on fresh datasets or tasks, has also shown promising results in brain tumor classification. However, challenges remain in brain tumor detection, such as the glioma and stroke tumors not being well contrasted, making segmentation and classification processes more challenging. Additionally, small tumor volume detection is still a challenge, as it can be detected as a normal region. Existing machine learning methods have limitations, and designing a lightweight model that provides high accuracy in less computational time is necessary.The fusion of multiple sequences and the use of CNN models have shown potential in glioma detection. The fused sequence provides more information compared to single sequences, and the suggested model has been trained on BRATS series for the detection of glioma.In conclusion, deep learning methods have significantly contributed to brain tumor detection, but there is still a need for a generic technique that performs well even with slight variations in training and testing images. Research can be conducted to detect brain tumors more accurately using real patient data from different image acquisition scanners. Keywords - Microscopic Brain Tumor Detection, 3D CNN, Feature Selection, MRI, Deep Learning, Tumor Classification, Feature Extraction.