FIXUS APP

 Project Description

🛑 The Problem: Challenges in X-Ray Diagnostics

Traditional X-ray analysis is often time-consuming and highly dependent on human expertise. Some key challenges include:

❌ Variability in X-ray views (AP, lateral, oblique) affecting diagnosis accuracy.

❌ Limited visual aids for pinpointing key areas of concern.

❌ Lack of AI-powered assistance to support medical professionals.

There was a need for an intelligent tool to enhance efficiency, precision, and accessibility in orthopedic diagnostics.

💡 The Solution: AI-Driven X-Ray Image Analysis

FIXUS-APP provides a seamless, user-friendly interface for uploading and analyzing X-ray images, offering heatmap visualizations to highlight critical areas.

✅ Automated X-ray Analysis – Upload an image and receive AI-generated insights.

🔥 Grad-CAM Heatmaps – Highlights key regions in the X-ray for better diagnostics.

📈 Pre-Trained InceptionV3 Model – Delivers high-accuracy predictions.

🖼️ Multi-View Adaptability – Handles AP, lateral, and oblique X-ray views.

This enables faster, more reliable interpretations, reducing human error and enhancing medical decision-making.

🖥️ Technology Stack

🌐 Flask - Backend framework for web app development

🖥️ Python - Drives ML model processing and integration

🏥 InceptionV3 - Pre-trained model for X-ray classification

🔥 Grad-CAM - Generates explainable AI heatmaps

👥 My Role & Collaboration

In this project:

🚀 Developed the web application - using Flask and Python.

🤖 Implemented machine learning models - for image classification and heatmap generation.

🤝 Collaborated with developers & ML experts - to optimize performance and accuracy.

⚡ Key Features & Benefits

AI-Powered Analysis - Enhances diagnostic accuracy with machine learning

Grad-CAM Heatmaps - Visualize key areas of interest in X-ray images

Multi-View Support - Adapts to different X-ray perspectives

Web-Based Interface - Accessible from any device without installation

🚀 Challenges & Solutions

🔹 Challenge: Handling different X-ray views (AP, lateral, oblique).

✅ Solution: Implemented custom image processing and dynamic heatmap adjustments to ensure accurate overlays.

🔹 Challenge: Ensuring precise and interpretable AI predictions.

✅ Solution: Used Grad-CAM for explainability, allowing doctors to trust and validate the AI’s findings.

📊 Impact & Applications

🏥 Improved Medical Diagnostics – Supports radiologists and orthopedic specialists.

🎓 Educational Tool – Helps medical students learn X-ray interpretation techniques.

⚡ Faster Diagnosis & Reduced Workload – Enhances efficiency for healthcare professionals.

🔗 Explore More

📂 GitHub Repository | 🖥️ Live Demo | 📖 Training Notebook | 📖 Dataset Augmentation