IMAGINOSIS: Deep Learning Disease Detection

Introduction

"IMAGINOSIS" is a revolutionary Deep Learning Disease Detection application that leverages AI algorithms to detect lung diseases, brain tumors, and tuberculosis by analyzing X-rays, CT scans, and MRI scans. The project aims to bridge the gap between AI advancements and healthcare, empowering healthcare professionals and patients with accurate, timely, and proactive disease detection.

Motivation

The project is driven by the aspiration to transform medical diagnostics using AI. "IMAGINOSIS" recognizes the critical role of early and precise disease detection in improving patient outcomes. The project seeks to harness the potential of deep learning to revolutionize medical diagnosis and enhance individual health management.

Key Objectives

• Rapid, reliable, and accurate diagnostic support for healthcare professionals.
• Timely interventions and enhanced patient care through AI-powered insights.
• Revolutionizing disease detection for lung diseases, brain tumors, and tuberculosis.

Features and Functionalities

• Multi-Disease Detection: Detects lung diseases, brain tumors, and tuberculosis.
• Image Analysis: Analyzes X-rays, CT scans, and MRI scans to identify anomalies.
• Accurate Diagnosis: Enhances diagnostic accuracy using AI algorithms.
• Timely Results: Provides rapid disease detection for prompt interventions.
• User-Friendly Interface: Offers an intuitive interface for healthcare professionals and patients.

Technology Stack

• Language: Java (Android Studio), Python (Google Colab)
• IDE: Android Studio
• Framework: TensorFlow

Challenges Faced

• Data Collection: Acquiring diverse and accurate medical imaging datasets.
• Model Complexity: Training accurate deep learning models with optimization challenges.
• Interpreting Medical Images: Fine-tuning models to decipher complex scans.
• Class Imbalance: Addressing imbalanced medical datasets for unbiased model performance.
• Model Deployment: Efficiently deploying trained models on a mobile app.

Future Enhancements

• Support for multi-modal imaging like ultrasound and PET scans.
• Adoption of a scalable cloud-based architecture.
• Continuous model learning with new medical data.
• Incorporating user feedback to refine models.
• Global accessibility with multiple languages and device compatibility.
• Collaboration with healthcare institutions for clinical trials.
• Extending capabilities to detect and diagnose rare medical conditions.

Summary

"IMAGINOSIS" is a pioneering project merging AI and healthcare to redefine diagnostics. The project's commitment to advancing medical science is evident in its innovative approach. "IMAGINOSIS" represents the potential of AI to positively impact medical diagnostics and healthcare, promising a healthier future for all.

YouTube: Watch Project Presentation

Dataset

• Lung Disease Analysis: Chest X-ray Dataset
• Brain Tumor Analysis: Brain Tumor MRI Dataset
• Tuberculosis Analysis: TB Chest X-ray Datasets