Uploaded "Vbai" family

Main Models/Vbai-1.0 Dementia/README.md

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+# Vbai-1.0 Dementia (11178564 parametre)
+
+## "Vbai-1.0 Dementia" modeli, hastanın demans durumunu MRI ve fMRI görüntüleri üzerine teşhis edebilecek şekilde eğitildi.
+
+## -----------------------------------------------------------------------------------
+
+# Vbai-1.0 Dementia (11178564 parameters)
+
+## The "Vbai-1.0 Dementia" model has been trained to diagnose the patient's dementia condition on MRI and fMRI images.
+
+# Kullanım / Usage
+
+```python
+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/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 = ['No Dementia', 'Mild Dementia', 'Avarage Dementia', 'Very Mild Dementia']
+
+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 = '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)
+```
+
+# Uygulama / As App
+
+```python
+import sys
+import torch
+import torch.nn as nn
+from torchvision import transforms, models
+from PIL import Image
+import matplotlib.pyplot as plt
+from PyQt5.QtWidgets import QApplication, QWidget, QPushButton, QLabel, QFileDialog, QVBoxLayout, QMessageBox
+from PyQt5.QtGui import QPixmap, QIcon
+from PyQt5.QtCore import Qt
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+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 = ['No Dementia', 'Mild Dementia', 'Avarage Dementia', 'Very Mild Dementia']
+
+
+class DementiaApp(QWidget):
+    def __init__(self):
+        super().__init__()
+        self.initUI()
+        self.model = None
+        self.image_path = None
+
+    def initUI(self):
+        self.setWindowTitle('Prediction App by Neurazum')
+        self.setWindowIcon(QIcon('C:/Users/eyupi/PycharmProjects/Neurazum/NeurAI/Assets/neurazumicon.ico'))
+        self.setGeometry(2500, 300, 400, 200)
+
+        self.loadModelButton = QPushButton('Upload Model', self)
+        self.loadModelButton.clicked.connect(self.loadModel)
+
+        self.loadImageButton = QPushButton('Upload Image', self)
+        self.loadImageButton.clicked.connect(self.loadImage)
+
+        self.predictButton = QPushButton('Make a Prediction', self)
+        self.predictButton.clicked.connect(self.predict)
+        self.predictButton.setEnabled(False)
+
+        self.resultLabel = QLabel('', self)
+        self.resultLabel.setAlignment(Qt.AlignCenter)
+
+        self.imageLabel = QLabel('', self)
+        self.imageLabel.setAlignment(Qt.AlignCenter)
+
+        layout = QVBoxLayout()
+        layout.addWidget(self.loadModelButton)
+        layout.addWidget(self.loadImageButton)
+        layout.addWidget(self.imageLabel)
+        layout.addWidget(self.predictButton)
+        layout.addWidget(self.resultLabel)
+
+        self.setLayout(layout)
+
+    def loadModel(self):
+        options = QFileDialog.Options()
+        fileName, _ = QFileDialog.getOpenFileName(self, "Choose Model Path", "",
+                                                  "PyTorch Model Files (*.pt);;All Files (*)", options=options)
+        if fileName:
+            self.model = models.resnet18(pretrained=False)
+            num_ftrs = self.model.fc.in_features
+            self.model.fc = nn.Linear(num_ftrs, 4)
+            self.model.load_state_dict(torch.load(fileName, map_location=device))
+            self.model = self.model.to(device)
+            self.model.eval()
+            self.predictButton.setEnabled(True)
+            QMessageBox.information(self, "Model Uploaded", "Model successfully uploaded!")
+
+    def loadImage(self):
+        options = QFileDialog.Options()
+        fileName, _ = QFileDialog.getOpenFileName(self, "Choose Image File", "",
+                                                  "Image Files (*.jpg *.jpeg *.png);;All Files (*)", options=options)
+        if fileName:
+            self.image_path = fileName
+            pixmap = QPixmap(self.image_path)
+            self.imageLabel.setPixmap(pixmap.scaled(224, 224, Qt.KeepAspectRatio))
+
+    def predict(self):
+        if self.model and self.image_path:
+            prediction, confidence = self.predictImage(self.image_path, self.model, transform)
+            self.resultLabel.setText(f'Prediction: {class_names[prediction]} (%{confidence * 100:.2f})')
+        else:
+            QMessageBox.warning(self, "Missing Information", "Model and picture must be uploaded.")
+
+    def predictImage(self, 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()
+
+
+if __name__ == '__main__':
+    app = QApplication(sys.argv)
+    ex = DementiaApp()
+    ex.show()
+    sys.exit(app.exec_())
+```
+
+# Python Sürümü / Python Version
+
+### 3.9 <=> 3.13
+
+# Modüller / Modules
+
+```bash
+matplotlib==3.8.0
+Pillow==10.0.1
+torch==2.3.1
+torchsummary==1.5.1
+torchvision==0.18.1
+```

Main Models/Vbai-1.0 Dementia/Vbai-1.0 Dementia.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c8316460eca34048f8aa055c38cef724059a51bc86ff07a64978cd000d2c331b
+size 44791484