Vbai Modelleri MRI ve fMRI görüntüleri üzerine eğitilmiştir. Bu modellerin eğitildiği veri setleri Neurazum tarafından gizli tutulmaktadır. Derin öğrenme yöntemleri ile eğitilerek çok yüksek doğruluk oranları ile MRI ve fMRI üzerinde çok hassas bir şekilde çalışabilir. Demans ile ilgili tüm beyin görselleriyle çalışıp, teşhis koyabilir. Nörobilim alanındaki geri kalmışlığa, ilkelliğe ve hata paylarına "bai" modelleriyle birlikte son vermeyi hedeflemektedir.
- Geliştirici: Neurazum
- Yayımcı: Eyüp İpler
- Model Tipi: MRI ve fMRI
- Lisans: CC-BY-NC-SA-4.0
Bu modellerdeki amacımız;
- Hastanın demans hastalıklarını (alzheimer gibi) daha erken ve daha doğru bir şekilde teşhis koymak,
- Hastanelerde çalışan doktorlara teşhis ve inceleme için kolaylık sağlamak,
- Risk taşıyan hastaları tespit etmek,
- Tanı koyulma aşamasında ki hata paylarını düşürmektir.
Klasik Kullanım:
import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
import matplotlib.pyplot as plt
import os
from torchsummary import summary
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.resnet18(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 4)
model.load_state_dict(torch.load('Vbai-1.0 Dementia/model/yolu'))
model = model.to(device)
model.eval()
summary(model, (3, 224, 224))
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])
])
class_names = ['Demans Değil', 'Hafif Demans', 'Orta Demans', 'Çok Hafif Demans']
def predict(image_path, model, transform):
image = Image.open(image_path).convert('RGB')
image = transform(image).unsqueeze(0).to(device)
model.eval()
with torch.no_grad():
outputs = model(image)
probs = torch.nn.functional.softmax(outputs, dim=1)
_, preds = torch.max(outputs, 1)
return preds.item(), probs[0][preds.item()].item()
def show_image_with_prediction(image_path, prediction, confidence, class_names):
image = Image.open(image_path)
plt.imshow(image)
plt.title(f"Tahmin: {class_names[prediction]} (%{confidence * 100:.2f})")
plt.axis('off')
plt.show()
test_image_path = 'MRI/veya/fMRI/görüntüsü'
prediction, confidence = predict(test_image_path, model, transform)
print(f'Tahmin: {class_names[prediction]} (%{confidence * 100})')
show_image_with_prediction(test_image_path, prediction, confidence, class_names)Vbai Modelleri;
- En büyük riski yanlış teşhis koymasıdır :),
- Herhangi bir kısıtlama bulunmamaktadır,
- Hastanın beyin görselleri hiçbir şekilde kişisel bilgi içermez. Bu nedenle, Vbai tarafından hiçbir şekilde kişisel veri elde edilemez.
- Görseller ne kadar yüksek çözünürlükte olursa o kadar iyidir.
- Modelin içeriğindeki gerekli modülleri kurmak için;
-
pip install -r requirements.txt
- Örnek kullanımla modelin ve veri setinin yolunu yerleştirin,
- Ve dosyayı çalıştırın.
- Vbai-1.0 Dementia => (Doğruluk oranı en az her ihtimalde = %90) (DEMANS DURUMLARI)
- Vbai-1.1 Dementia => (Doğruluk oranı en az her ihtimalde = 90%) (DEMANS DURUMLARI)
Özetle Vbai modelleri, hastanın demans durumunu tespit ederek tıp alanında çalışanlara kolaylık sağlamak amacıyla teşhis koyabilen görüntü işleme modelidir.
LinkedIn: https://www.linkedin.com/company/neurazum
Eyüp İpler - https://www.linkedin.com/in/eyupipler/
Vbai models were trained on MRI and fMRI images. The data sets on which these models are trained are kept confidential by Neurazum. It can work very precisely on MRI and fMRI with very high accuracy rates by training with deep learning methods. It can work with all brain images related to dementia and diagnose. It aims to put an end to the backwardness, primitiveness and error margins in the field of neuroscience with ‘bai’ models.
- Developed by: Neurazum
- Shared by: Eyüp İpler
- Model type: MRI and fMRI
- License: CC-BY-NC-SA-4.0
Our aim in these models is to;
- To diagnose the patient's dementia diseases (such as Alzheimer's) earlier and more accurately,
- Providing convenience to doctors working in hospitals for diagnosis and examination,
- Identifying patients at risk,
- to reduce the margin of error in the diagnostic process.
Classical Use:
import torch
import torch.nn as nn
from torchvision import transforms, models
from PIL import Image
import matplotlib.pyplot as plt
import os
from torchsummary import summary
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = models.resnet18(pretrained=False)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, 4)
model.load_state_dict(torch.load('Vbai-1.0 Dementia/model/path'))
model = model.to(device)
model.eval()
summary(model, (3, 224, 224))
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])
])
class_names = ['Non Demented', 'Mild Demented', 'Moderate Demented', 'Very Mild Demented']
def predict(image_path, model, transform):
image = Image.open(image_path).convert('RGB')
image = transform(image).unsqueeze(0).to(device)
model.eval()
with torch.no_grad():
outputs = model(image)
probs = torch.nn.functional.softmax(outputs, dim=1)
_, preds = torch.max(outputs, 1)
return preds.item(), probs[0][preds.item()].item()
def show_image_with_prediction(image_path, prediction, confidence, class_names):
image = Image.open(image_path)
plt.imshow(image)
plt.title(f"Prediction: {class_names[prediction]} (%{confidence * 100:.2f})")
plt.axis('off')
plt.show()
test_image_path = 'MRI/or/fMRI/image/path'
prediction, confidence = predict(test_image_path, model, transform)
print(f'Prediction: {class_names[prediction]} (%{confidence * 100})')
show_image_with_prediction(test_image_path, prediction, confidence, class_names)Vbai Models;
- The biggest risk is misdiagnosis :),
- There are no restrictions,
- The patient's brain images do not contain any personal information. Therefore, no personal data can be obtained by Vbai in any way.
- The higher the resolution of the visuals, the better.
- To install the necessary modeules in the model;
-
pip install -r requirements.txt
- Place the path of the model in the example uses.
- And run the file.
- Vbai-1.0 Dementia => (Accuracy rate at least in all probability = 90%) (DEMENTIA STATES)
- Vbai-1.1 Dementia => (Accuracy rate at least in all probability = 90%) (DEMENTIA STATES)
In summary, Vbai models are image processing models that can diagnose the patient's dementia status in order to provide convenience to medical professionals.
LinkedIn: https://www.linkedin.com/company/neurazum
Eyüp İpler - https://www.linkedin.com/in/eyupipler/