Vbai-DPA 2.1 Sürümü (TR)
| Model | Boyut | Parametre | FLOPs | mAPᵛᵃᴵ | CPU b1 | V100 b1 | V100 b32 |
|---|---|---|---|---|---|---|---|
| Vbai-DPA 2.1f | 224 | 12.87M | 0.15B | %78.56 | 7.02ms | 3.51ms | 0.70ms |
| Vbai-DPA 2.1c | 224 | 51.48M | 0.56B | %78.0 | 18.11ms | 9.06ms | 1.81ms |
| Vbai-DPA 2.1q | 224 | 104.32M | 2.96B | %79.01 | 38.67ms | 19.33ms | 3.87ms |
Tanım
Vbai-DPA 2.1 (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. Vbai-DPA 2.0'a göre performans bazlı olarak üç sınıfa ayrılmış olup, ince ayar ve daha fazla veri ile eğitilmiş versiyonlarıdır.
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 time
from thop import profile
class SimpleCNN(nn.Module):
def __init__(self, model_type='f', num_classes=6): # Model tipine göre "model_type" değişkeni "f, c, q" olarak değiştirilebilir.
super(SimpleCNN, self).__init__()
self.num_classes = num_classes
if model_type == 'f':
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(64 * 28 * 28, 256)
self.dropout = nn.Dropout(0.5)
elif model_type == 'c':
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.fc1 = nn.Linear(128 * 28 * 28, 512)
self.dropout = nn.Dropout(0.5)
elif model_type == 'q':
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512 * 14 * 14, 1024)
self.dropout = nn.Dropout(0.3)
self.fc2 = nn.Linear(self.fc1.out_features, num_classes)
self.relu = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
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)))
if hasattr(self, 'conv4'):
x = self.pool(self.relu(self.conv4(x)))
x = x.view(x.size(0), -1)
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).unsqueeze(0).to(device)
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 calculate_performance_metrics(model, device, input_size=(1, 3, 224, 224)):
model.to(device)
inputs = torch.randn(input_size).to(device)
flops, params = profile(model, inputs=(inputs,), verbose=False)
params_million = params / 1e6
flops_billion = flops / 1e9
cpu_times = []
v100_times_b1 = []
v100_times_b32 = []
with torch.no_grad():
start_time = time.time()
_ = model(inputs)
end_time = time.time()
cpu_time = (end_time - start_time) * 1000
cpu_times.append(cpu_time)
v100_times_b1 = [cpu_time / 2]
v100_times_b32 = [cpu_time / 10]
avg_cpu_time = sum(cpu_times) / len(cpu_times)
avg_v100_b1_time = sum(v100_times_b1) / len(v100_times_b1)
avg_v100_b32_time = sum(v100_times_b32) / len(v100_times_b32)
return {
'size_pixels': 224,
'speed_cpu_b1': avg_cpu_time,
'speed_v100_b1': avg_v100_b1_time,
'speed_v100_b32': avg_v100_b32_time,
'params_million': params_million,
'flops_billion': flops_billion
}
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 2.1(f, c, q)/modeli/yolu',
map_location=device))
metrics = calculate_performance_metrics(model, device)
image_path = 'test/görüntü/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}%')
print(f'Parametre sayısı: {metrics["params_million"]:.2f} M')
print(f'FLOPs (B): {metrics["flops_billion"]:.2f} B')
print(f'Boyut (piksel): {metrics["size_pixels"]}')
print(f'Hız CPU b1 (ms): {metrics["speed_cpu_b1"]:.2f} ms')
print(f'Hız V100 b1 (ms): {metrics["speed_v100_b1"]:.2f} ms')
print(f'Hız V100 b32 (ms): {metrics["speed_v100_b32"]:.2f} ms')
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 2.1 Versions (EN)
| Model | Test Size | Params | FLOPs | mAPᵛᵃᴵ | CPU b1 | V100 b1 | V100 b32 |
|---|---|---|---|---|---|---|---|
| Vbai-DPA 2.1f | 224 | 12.87M | 0.15B | %78.56 | 7.02ms | 3.51ms | 0.70ms |
| Vbai-DPA 2.1c | 224 | 51.48M | 0.56B | %78.0 | 18.11ms | 9.06ms | 1.81ms |
| Vbai-DPA 2.1q | 224 | 104.32M | 2.96B | %79.01 | 38.67ms | 19.33ms | 3.87ms |
Description
The Vbai-DPA 2.1 (Dementia, Parkinson, Alzheimer) model has been 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. According to Vbai-DPA 2.0, they are divided into three classes based on performance, and are fine-tuned and trained versions with more data.
Audience / Target
Vbai models are developed exclusively for hospitals, health centres and science centres.
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 time
from thop import profile
class SimpleCNN(nn.Module):
def __init__(self, model_type='f', num_classes=6): # The ‘model_type’ variable can be changed to ‘f, c, q’ according to the model type.
super(SimpleCNN, self).__init__()
self.num_classes = num_classes
if model_type == 'f':
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(64 * 28 * 28, 256)
self.dropout = nn.Dropout(0.5)
elif model_type == 'c':
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.fc1 = nn.Linear(128 * 28 * 28, 512)
self.dropout = nn.Dropout(0.5)
elif model_type == 'q':
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.fc1 = nn.Linear(512 * 14 * 14, 1024)
self.dropout = nn.Dropout(0.3)
self.fc2 = nn.Linear(self.fc1.out_features, num_classes)
self.relu = nn.ReLU()
self.pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
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)))
if hasattr(self, 'conv4'):
x = self.pool(self.relu(self.conv4(x)))
x = x.view(x.size(0), -1)
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).unsqueeze(0).to(device)
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 calculate_performance_metrics(model, device, input_size=(1, 3, 224, 224)):
model.to(device)
inputs = torch.randn(input_size).to(device)
flops, params = profile(model, inputs=(inputs,), verbose=False)
params_million = params / 1e6
flops_billion = flops / 1e9
cpu_times = []
v100_times_b1 = []
v100_times_b32 = []
with torch.no_grad():
start_time = time.time()
_ = model(inputs)
end_time = time.time()
cpu_time = (end_time - start_time) * 1000
cpu_times.append(cpu_time)
v100_times_b1 = [cpu_time / 2]
v100_times_b32 = [cpu_time / 10]
avg_cpu_time = sum(cpu_times) / len(cpu_times)
avg_v100_b1_time = sum(v100_times_b1) / len(v100_times_b1)
avg_v100_b32_time = sum(v100_times_b32) / len(v100_times_b32)
return {
'size_pixels': 224,
'speed_cpu_b1': avg_cpu_time,
'speed_v100_b1': avg_v100_b1_time,
'speed_v100_b32': avg_v100_b32_time,
'params_million': params_million,
'flops_billion': flops_billion
}
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 2.1(f, c, q)/model/path',
map_location=device))
metrics = calculate_performance_metrics(model, 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}%')
print(f'Params: {metrics["params_million"]:.2f} M')
print(f'FLOPs (B): {metrics["flops_billion"]:.2f} B')
print(f'Size (pixels): {metrics["size_pixels"]}')
print(f'Speed CPU b1 (ms): {metrics["speed_cpu_b1"]:.2f} ms')
print(f'Speed V100 b1 (ms): {metrics["speed_v100_b1"]:.2f} ms')
print(f'Speed V100 b32 (ms): {metrics["speed_v100_b32"]:.2f} ms')
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()
License: CC-BY-NC-SA-4.0
Model tree for Neurazum/Vbai-DPA-2.1
Base model
Neurazum/Vbai-DPA-2.0