Brain tumor detection using with GW Deep CNN LSTM

Brain tumors are recognized as severe illnesses wherein the precision of images plays a crucial role. Accurately identifying tumors is essential for precisely pinpointing the affected area and thereby reducing the mortality rate. Consequently, understanding the hidden patterns becomes imperative for an improved diagnosis and image quality. However, achieving accurate diagnoses across various lesion cases poses a significant challenge. To address the limitations of existing methods, To be able to automatically detect brain cancers from MRI scans, we introduce a hybrid architecture dubbed Deep Convolutional Neural Network with LSTM (Deep CNN&LSTM) that makes utilizes a Gaussian filter. There are several layers in the Deep CNN&LSTM approach, and each one is essential to the processing pipeline. Brain MRI pictures are first obtained from a certain database. In order to improve the clarity and quality of the images, a Gaussian weighted nonlocal mean filter is used during the preprocessing stage to eliminate noisy pixels. After preprocessing, the image is segmented using U net segmentation approach, which divide the picture into areas with comparable features isolating areas of interest, such as possible tumor locations, requires this procedure. An LSTM network is then fed the retrieved features to classify the data. Because of the capabilities of the LSTM, it is possible to analyze the sequential nature of the extracted features and accurately determine if the input image is normal or contains a tumor. The Deep CNN-LSTM architecture is exceptional in detecting diseases with high accuracy while reducing processing time and error rate. Because of its effectiveness, it holds great promise for the automatic identification of brain tumors, facilitating quicker and more accurate clinical judgment.