Ali Hacks 2024

October 2024🏆 1st Place Winner24-Hour Hackathon

AI-Powered Healthcare Diagnostic System

24 hours • 3 team members • 1 breakthrough solution

View on GitHub Devpost Submission Live Demo

The Challenge

Create an innovative solution that leverages AI to solve real-world problems in healthcare accessibility. Our team developed a diagnostic system that uses computer vision and natural language processing to provide preliminary health assessments from symptom descriptions and medical imagery.

Interactive ML Training Demo

Neural Network Training Visualization

Model Accuracy

0.00%

Solution Architecture

📷

Image Input

Medical imagery processing

→

🧠

CNN Model

Feature extraction

→

📊

Analysis

Pattern matching

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📋

Results

Diagnostic report

Code Implementation

diagnostic_model.py

import tensorflow as tf
from tensorflow.keras import layers, models
import numpy as np

class DiagnosticModel:
    def __init__(self):
        self.model = self._build_model()
        self.confidence_threshold = 0.85
        
    def _build_model(self):
        """Build CNN architecture for medical image analysis"""
        model = models.Sequential([
            layers.Conv2D(32, (3, 3), activation='relu', 
                         input_shape=(224, 224, 3)),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(64, (3, 3), activation='relu'),
            layers.MaxPooling2D((2, 2)),
            layers.Conv2D(128, (3, 3), activation='relu'),
            layers.GlobalAveragePooling2D(),
            layers.Dropout(0.5),
            layers.Dense(256, activation='relu'),
            layers.Dense(5, activation='softmax')  # 5 diagnostic categories
        ])
        
        model.compile(
            optimizer='adam',
            loss='categorical_crossentropy',
            metrics=['accuracy', 'AUC']
        )
        return model
    
    def predict_with_confidence(self, image):
        """Make prediction with confidence score"""
        processed_img = self.preprocess_image(image)
        predictions = self.model.predict(processed_img)
        
        max_confidence = np.max(predictions)
        diagnosis = np.argmax(predictions)
        
        if max_confidence < self.confidence_threshold:
            return {
                'diagnosis': 'Inconclusive',
                'confidence': max_confidence,
                'recommend': 'Consult healthcare professional'
            }
        
        return {
            'diagnosis': self.decode_diagnosis(diagnosis),
            'confidence': max_confidence,
            'severity': self.assess_severity(predictions)
        }