Brain Diseases Detection and Prediction Using DeepQ Convolution Neural Network in Colab

Purpose: The paper aims to analyze the detection and prediction of brain diseases for future betterment using Convolutional neural network. Objectives: main objective this journal is find most correct technique detective work in various like Alzheimer’s disease tumours machine learning deep learning-based approaches. Methodology: An automatic tool neoplasm classification based on magnetic resonance imaging information given wherever sample image slices are fed a convolutional network (CNN) supported by ResNet Squeeze Excitation model. Alzheimer's misdetection system Neural Network design (MRI) scan images. Results: Create an app-based user interface hospitals that enables medical professionals quickly determine effects recommend treatments. We can attempt make predictions about location severity mental illnesses from volume-based 3-D images because performance complexity ConvNets depend input data visualisation. Improvements made surgery planning, education, computer guidance creating anatomical models specific patients. Originality/Value: results provide brief overview with better improved form accurately. Type Paper: Conceptual research paper.