Abstract

Medical imaging plays a vital role in modern healthcare for diagnosing diseases and analysing patient conditions. With the advancement of artificial intelligence and deep learning techniques, medical images can now be automatically analyzed to extract useful information. One such application is predicting demographic attributes such as age and gender from chest X-ray images.

This project focuses on predicting a person's age and gender using chest X-ray scans by applying Convolutional Neural Networks (CNN), a powerful deep learning technique used for image processing and feature extraction. The dataset used in this project consists of approximately 10,700 training images and 11,700 testing images of chest X-ray scans obtained from a dataset provided through a Kaggle competition organized by the Radiological Society of São Paulo and Amazon Web Services.

The project involves several steps including image pre-processing, feature extraction, classification, and regression. Gender prediction is treated as an image classification problem, while age prediction is treated as a regression problem. The CNN model learns visual patterns and structural features from lung scans to make predictions.

Finally, the trained model is deployed using a Flask web application, where users can upload a chest X-ray image and obtain predicted age and gender results. This project demonstrates the application of deep learning techniques in medical image analysis and healthcare technology.

2. Objectives

The main objectives of this project are:

1. To understand medical image analysis using machine learning techniques.
2. To study chest X-ray images and their visual features.
3. To learn image pre-processing and cleaning techniques.
4. To understand the concept of image classification.
5. To understand regression techniques for predicting continuous values such as age.
6. To study and implement Convolutional Neural Networks (CNN).
7. To train a CNN model for predicting age and gender from chest X-ray images.
8. To deploy the trained model as a Flask-based web application.

3. Existing System

Traditional medical systems rely on manual analysis of chest X-ray images by medical professionals to interpret patient characteristics and health conditions.

Existing approaches generally involve:

• Manual visual examination by radiologists
• Medical report analysis
• Basic statistical and rule-based systems

Limitations of Existing Systems

1. Manual analysis is time-consuming and requires expert knowledge.
2. Human interpretation may vary between different medical professionals.
3. Difficult to analyse large volumes of medical image data.
4. Lack of automated systems for extracting demographic information like age and gender from X-ray scans.
5. Limited use of advanced artificial intelligence methods in traditional systems.

These limitations highlight the need for automated deep learning-based systems for analysing medical images.