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