Develop two variant of deep learning image classification

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Reference no: EM133539762

Introduction to Artificial Intelligence

Objectives

Assessment 3 is an individual assessment. The purpose of this assignment is to extend your knowledge of the concepts covered in this unit about different facets of AI. You are required to write and execute python code for the given tasks. You are also required to write a report which will have python code, output screenshots showing the answers to the questions, and an analysis of the generated outputs.

Task Description

Task: Build AI (deep learning) based Image classification models

Objective: Construct precise image classification model to determine from chest X-ray images whether or not a person has pneumonia.

Description: The normal chest X-ray depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia typically exhibits a focal lobar consolidation whereas viral pneumonia manifests with a more diffuse interstitial pattern in both lungs. Now a days, Artificial Intelligence techniques are widely used to identity pneumonia from chest X-ray images. Since it is a matter of life, accuracy is essential for such AI based image classification model. In this task, you need to use Kaggle chest X-ray images dataset and build deep learning) based image classification models. This task will introduce you to essential techniques in image processing and Artificial intelligence.

You are required to divide AI-based image classification task into the following subtasks:

• Data Collection and exploration: Download the chest X-ray dataset from Kaggle and save it in your local directory.

• Pre-processing: Perform necessary pre-processing steps on the dataset, such as resizing images, converting grayscale to 3 channels, and normalization.

• Model development: Develop two variant of deep learning image classification models to distinguish normal and pneumonia images from the given dataset. Split the dataset into training, validation and testing sets and train the model on the training set.

• Evaluation: Evaluate the performance of the classification model on the testing set.

Report: Write a report which will have python code, output screenshots and an analysis of the generated outputs addressing all of the above subtasks.

Reference no: EM133539762

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