AI Driven Disease Detection – Using Deep Learning

Abstract

Our project utilizes deep learning and CNN models to revolutionize disease detection from medical images. By harnessing these advanced algorithms, we enhance the accuracy of diagnostics, particularly in X-rays, CT scans, and MRI scans. This streamlined approach represents a significant advancement in healthcare, showcasing the potential of artificial intelligence to reshape and optimize disease identification.

Introduction

In the realm of medical diagnostics, we're pioneering a transformative approach using deep learning and convolutional neural networks (CNNs). Our project focuses on leveraging these technologies for accurate disease detection through the analysis of medical images like X-rays, CT scans, and MRI scans. This innovative integration aims to elevate diagnostic precision and efficiency, ushering in a new era in healthcare.

Key Objectives

1. User-Friendly Diagnosis: Simplify and streamline diagnostic processes for users.
2. Data Collection for Research: Gather anonymized medical data for research purposes.
3. Contribution to Medical Knowledge: Contribute to advancements in medical understanding and treatment.
4. Enhancing User Experience: Prioritize a practical and user-friendly app interface.
5. Collaborative Healthcare Approach: Foster collaboration between users and medical researchers.

Features and Functionalities

• Advanced Deep Learning: Utilizes advanced deep learning algorithms for the detection of medical conditions, including:
  • Lung diseases
  • Brain tumors
• Image Analysis: Analyses medical images (X-rays, CT scans, and MRI scans) to identify anomalies and patterns indicative of diseases.
• Accurate Diagnosis: Enhances diagnostic accuracy using AI, surpassing human capabilities and minimizing the risk of misinterpretation.
• Timely Results: Provides rapid disease detection, enabling healthcare professionals to make informed decisions promptly and initiate timely interventions.
• User-Friendly Interface: Boasts an intuitive and user-friendly interface for seamless navigation for both healthcare professionals and patients.

Project Features

• Focused Analysis: Developed for brain tumor and chest analysis.
• Utilizing Similar Principles: Leverages similar principles of advanced deep learning for precise disease identification.
• Streamlined Functionality: Prioritizes a focused and streamlined functionality for efficient analysis.
• User-Centric Design: Ensures a user-friendly interface catering to both healthcare professionals and patients.

Technology Stack

• Android App Development:
  • Language: Java
  • IDE: Android Studio
• Deep Learning:
  • Language: Python
  • Environment: Google Colab
  • Framework: TensorFlow
• Deep Learning Techniques:
  • Leveraging Deep Learning and CNN (Convolutional Neural Networks) for model development and image analysis.

Dataset and Model

• Lung Disease Analysis: Chest X-ray Dataset
• Brain Tumor Analysis: Brain Tumor MRI Dataset
• (sorry for the confusion, the size of the dataset is actually 5840 for chest and 7000 for brain tumor- please do consider this)
• Model Used: CNN (Convolutional Neural Network)

Project Demo Video

Watch the Project Demo Video