Vbai-DPA 2.0 (TR)

Piksel Boyutu: 224
Parametre: 51,477,062
FLOPs (B): 0.56
CPU b1: 18.11ms
V100 b1: 9.06ms
V100 b32: 1.81ms

Tanım

Vbai-DPA 2.0 (Dementia, Parkinson, Alzheimer) modeli, MRI veya fMRI görüntüsü üzerinden beyin hastalıklarını teşhis etmek amacıyla eğitilmiş ve geliştirilmiştir. Hastanın parkinson olup olmadığını, demans durumunu ve alzheimer riskini yüksek doğruluk oranı ile göstermektedir.

Kitle / Hedef

Vbai modelleri tamamen öncelik olarak hastaneler, sağlık merkezleri ve bilim merkezleri için geliştirilmiştir.

Sınıflar

Alzheimer Hastası: Hasta kişi, kesinlikle alzheimer hastasıdır.
Ortalama Alzheimer Riski: Hasta kişi, yakın bir zamanda alzheimer olabilir.
Hafif Alzheimer Riski: Hasta kişinin, alzheimer olması için biraz daha zamanı vardır.
Çok Hafif Alzheimer Riski: Hasta kişinin, alzheimer seviyesine gelmesine zaman vardır.
Risk Yok: Kişinin herhangi bir riski bulunmamaktadır.
Parkinson Hastası: Kişi, parkinson hastasıdır.

Kullanım

import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
import os


class SimpleCNN(nn.Module):
    def __init__(self, num_classes=6):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, num_classes)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = x.view(-1, 128 * 28 * 28)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x


def predict_image(model, image_path, transform, device):
    image = Image.open(image_path).convert('RGB')
    image = transform(image)
    image = image.unsqueeze(0)

    model.eval()

    with torch.no_grad():
        image = image.to(device)
        outputs = model(image)
        _, predicted = torch.max(outputs, 1)
        probabilities = torch.nn.functional.softmax(outputs, dim=1)
        confidence = probabilities[0, predicted].item() * 100

    return predicted.item(), confidence, image


def main():
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = SimpleCNN(num_classes=6).to(device)
    model.load_state_dict(
        torch.load('Vbai-DPA 2.0/model/yolu',
                   map_location=device))

    image_path = 'test/görüntüsü/yolu'

    predicted_class, confidence, image = predict_image(model, image_path, transform, device)

    class_names = ['Alzheimer Hastası', 'Hafif Alzheimer Riski', 'Ortalama Alzheimer Riski', 'Çok Hafif Alzheimer Riski',
                   'Risk Yok', 'Parkinson Hastası']

    print(f'Tahmin edilen sınıf: {class_names[predicted_class]}')
    print(f'Doğruluk: {confidence}%')
    total_params = sum(p.numel() for p in model.parameters())
    print(f'Parametre sayısı: {total_params}')

    plt.imshow(image.squeeze(0).permute(1, 2, 0))
    plt.title(f'Tahmin: {class_names[predicted_class]} \nDoğruluk: {confidence:.2f}%')
    plt.axis('off')
    plt.show()


if __name__ == '__main__':
    main()

Lisans: CC-BY-NC-SA-4.0

----------------------------------------

Vbai-DPA 2.0 (EN)

Pixel Size: 224
Parameters: 51,477,062
FLOPs (B): 0.56
CPU b1: 18.11ms
V100 b1: 9.06ms
V100 b32: 1.81ms

Description

Vbai-DPA 2.0 (Dementia, Parkinson, Alzheimer) model is trained and developed to diagnose brain diseases through MRI or fMRI images. It shows whether the patient has Parkinson's disease, dementia status and Alzheimer's risk with high accuracy.

Audience / Target

Vbai models are developed entirely for hospitals, health centres and science centres as a priority.

Classes

Alzheimer's disease: The sick person definitely has Alzheimer's disease.
Average Risk of Alzheimer's Disease: The sick person may develop Alzheimer's disease in the near future.
Mild Alzheimer's Risk: The sick person has a little more time to develop Alzheimer's disease.
Very Mild Alzheimer's Risk: The sick person has time to reach the level of Alzheimer's disease.
No Risk: The person does not have any risk.
Parkinson's Disease: The person has Parkinson's disease.

Usage

import torch
import torch.nn as nn
from torchvision import transforms
from PIL import Image
import matplotlib.pyplot as plt
import os


class SimpleCNN(nn.Module):
    def __init__(self, num_classes=6):
        super(SimpleCNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, num_classes)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = x.view(-1, 128 * 28 * 28)
        x = self.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return x


def predict_image(model, image_path, transform, device):
    image = Image.open(image_path).convert('RGB')
    image = transform(image)
    image = image.unsqueeze(0)

    model.eval()

    with torch.no_grad():
        image = image.to(device)
        outputs = model(image)
        _, predicted = torch.max(outputs, 1)
        probabilities = torch.nn.functional.softmax(outputs, dim=1)
        confidence = probabilities[0, predicted].item() * 100

    return predicted.item(), confidence, image


def main():
    transform = transforms.Compose([
        transforms.Resize((224, 224)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model = SimpleCNN(num_classes=6).to(device)
    model.load_state_dict(
        torch.load('Vbai-DPA 2.0/model/path',
                   map_location=device))

    image_path = 'test/image/path'

    predicted_class, confidence, image = predict_image(model, image_path, transform, device)

    class_names = ['Alzheimer Disease', 'Mild Alzheimer Risk', 'Moderate Alzheimer Risk', 'Very Mild Alzheimer Risk',
                   'No Risk', 'Parkinson Disease']

    print(f'Predicted Class: {class_names[predicted_class]}')
    print(f'Accuracy: {confidence}%')
    total_params = sum(p.numel() for p in model.parameters())
    print(f'Params: {total_params}')

    plt.imshow(image.squeeze(0).permute(1, 2, 0))
    plt.title(f'Prediction: {class_names[predicted_class]} \nAccuracy: {confidence:.2f}%')
    plt.axis('off')
    plt.show()


if __name__ == '__main__':
    main()

Neurazum
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Vbai-DPA-2.0