AI-Powered Dementia Detection: Revolutionizing Healthcare with BrainTechAI

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In a groundbreaking achievement, Team Astrix has developed an AI-powered dementia detection system that promises to revolutionize healthcare. Their project, BrainTech.AI, was a finalist at Diversion 2k25, showcasing the potential of AI in medical diagnostics. The team built a platform that not only detects dementia with 99% accuracy from MRI scans but also connects patients with specialists, generates digital prescriptions, and offers a mental health quiz for early detection.

Project Links:

You Should Know:

Here are some practical commands and codes related to AI, healthcare, and MRI analysis that you can use to explore similar projects:

1. Python Code for MRI Image Preprocessing:

import numpy as np
import cv2
from skimage import exposure

def preprocess_mri(image_path):

<h1>Load MRI image</h1>

image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

<h1>Enhance contrast</h1>

image = exposure.equalize_hist(image)

<h1>Normalize the image</h1>

image = image / 255.0

return image
  1. Training a Simple AI Model for Image Classification (TensorFlow/Keras): 
    import tensorflow as tf
from tensorflow.keras import layers, models</li>
</ol>

def build_model(input_shape):
model = models.Sequential([
layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape),
layers.MaxPooling2D((2, 2)),
layers.Conv2D(64, (3, 3), activation='relu'),
layers.MaxPooling2D((2, 2)),
layers.Flatten(),
layers.Dense(64, activation='relu'),
layers.Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
return model

    3. Linux Command for Processing Medical Data:

    
<h1>Convert DICOM files to PNG for easier processing</h1>

for file in *.dcm; do
dcmj2pnm $file "${file%.dcm}.png"
done

    4. Windows PowerShell Command for Managing Healthcare Data:

    
<h1>Bulk rename MRI files for organization</h1>

Get-ChildItem -Path "C:\MRIs" -Filter *.dcm | Rename-Item -NewName { $_.Name -replace ' ', '_' }

    5. Docker Command for Deploying AI Models:

    
<h1>Run a TensorFlow serving container for your AI model</h1>

docker run -p 8501:8501 --name braintech_ai --mount type=bind,source=/path/to/model,target=/models/braintech -e MODEL_NAME=braintech -t tensorflow/serving

    What Undercode Say:

    AI in healthcare is no longer a futuristic concept—it’s here, and it’s transforming lives. Team Astrix’s BrainTech.AI is a testament to how innovation, teamwork, and technology can address real-world problems like dementia detection. By leveraging AI, MRI analysis, and digital platforms, they’ve created a solution that is not only efficient but also accessible.

    For those interested in exploring similar projects, the provided Python scripts, Linux commands, and Docker setups can serve as a starting point. Whether you’re preprocessing MRI data, training AI models, or deploying them in production, the tools and techniques are within reach. The future of healthcare is digital, and with AI, the possibilities are endless.

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    References:

    Reported By: Rudrasish Dutta – Hackers Feeds
    Extra Hub: Undercode MoN
    Basic Verification: Pass ✅Featured Image

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