Cnn–lstm Hybrid Architecture for Robust Brain Tumor Detection and Analysis

Timely and proper diagnosis of brain tumor is essential at an early stage to enhance patient survival and treatment strategies. The application of deep learning to medical imaging has been quite promising, but most of the existing methods use exclusively convolutional networks that do not exploit time in sequential imaging data and therefore do not consider temporal relationships in medical images. The proposed method CNNLSTM hybrid architecture that combines extraction of spatial features and sequence learning to effectively model brain tumors and analyze them out of MRI scans. The CNN component effectively captures high-level spatial patterns in tumor regions whereas the LSTM network captures sequential dependencies across slices to model contextual patterns. The suggested approach is compared with four publicly available baseline methods: VGG16, ResNet50, DenseNet121, and InceptionV3 on a publicly available brain MRI dataset containing glioma, meningioma, pituitary tumor, and normal samples. Experimental findings have shown that CNNLSTM model performs better with 97.8, 97.4, 98.1, and 97.7 accuracy, precision, recall, and F1-score respectively, gaining better results than VGG16 (92.3%), ResNet50 (94.5%), and DenseNet121 (95.8) and InceptionV3 (94.9). These findings underscore the merit of using both convolutional and recurrent networks to encapsulate both spatial and sequential features, thus facilitating more accurate brain tumor diagnosis.