{ "cells": [ { "cell_type": "markdown", "id": "81f27e7e-c4eb-4ac5-9af0-b09461a37105", "metadata": {}, "source": [ "# Proyecto Detección de Cáncer Cerebral para diagnóstico médico.\n", "\n", "## Acerca del conjunto de datos\n", "\n", "### Cáncer cerebral\n", "Conjunto de datos de resonancia magnética\n", "\n", "Este conjunto de datos contiene una colección completa de imágenes de resonancia magnética para la investigación del cáncer cerebral, específicamente destinadas a respaldar el diagnóstico médico.\n", "\n", "## Categorías\n", "- Educación médica\n", "- Cáncer cerebral\n", "- Aprendizaje automático\n", "- Clasificación de imágenes\n", "- Áreas del cerebro\n", "- Aprendizaje profundo\n", "\n", "### Inicio del proyecto\n", "Previamente ha sido ejecutado un script para la separación adecuada del conjunto de imágenes en entrenamiento, test y validación.\n", "\n", "- 70% Entrenamiento\n", "- 15% Test\n", "- 15% Validación\n", "\n", "### Comprobar que usamos GPU" ] }, { "cell_type": "code", "execution_count": 1, "id": "bbe089d8-9208-4c7f-a1e4-9c3376ae9204", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "NVIDIA GeForce MX150\n" ] } ], "source": [ "import torch\n", "print(torch.cuda.is_available()) # → True si está OK\n", "print(torch.cuda.get_device_name(0)) # → Nombre de tu GPU (ej. NVIDIA GeForce RTX 3060)" ] }, { "cell_type": "markdown", "id": "48c92fed-76dc-475a-91f1-004aaf9c9bf4", "metadata": {}, "source": [ "# Módulos" ] }, { "cell_type": "code", "execution_count": 2, "id": "37261220-a2b3-444e-b69c-2ee7b5603fe9", "metadata": {}, "outputs": [], "source": [ "from torchvision import transforms # Transforma el dataset\n", "\n", "from torchvision.datasets import ImageFolder\n", "from torch.utils.data import DataLoader\n", "\n", "import torch.nn as nn # Arquitectura de Red\n", "import torch.nn.functional as F\n", "\n", "import torch\n", "from torch import optim\n", "\n", "from torch.optim.lr_scheduler import StepLR\n", "\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "id": "b5ea1a3e-6a3b-4573-8ecf-ead1aab76674", "metadata": {}, "source": [ "# Transformación" ] }, { "cell_type": "code", "execution_count": 11, "id": "14842a89-53e6-4cca-b606-d7f8743cc03c", "metadata": {}, "outputs": [], "source": [ "transformaciones_train = transforms.Compose([\n", " transforms.RandomResizedCrop(224),\n", " transforms.RandomHorizontalFlip(),\n", " transforms.RandomRotation(15),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "transformaciones_test = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", "])" ] }, { "cell_type": "markdown", "id": "1b4812ae-b868-4edb-ab69-3f4422d9e7f7", "metadata": {}, "source": [ "# Carga del Set de Datos" ] }, { "cell_type": "code", "execution_count": 12, "id": "7caca151-ba9e-4254-8c42-85bd4868456b", "metadata": {}, "outputs": [], "source": [ "train_dataset = ImageFolder('data/train', transform=transformaciones_train)\n", "val_dataset = ImageFolder('data/val', transform=transformaciones_test)\n", "test_dataset = ImageFolder('data/test', transform=transformaciones_test)\n", "\n", "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n", "val_loader = DataLoader(val_dataset, batch_size=32)\n", "test_loader = DataLoader(test_dataset, batch_size=32)" ] }, { "cell_type": "markdown", "id": "3323d006-731c-42ae-b857-6d484933c06f", "metadata": {}, "source": [ "# Definir Arquitectura" ] }, { "cell_type": "code", "execution_count": 3, "id": "c80c09aa-8dd7-43fd-96b0-d6ba7450a9a1", "metadata": {}, "outputs": [], "source": [ "class SimpleCNN(nn.Module):\n", " def __init__(self):\n", " super(SimpleCNN, self).__init__()\n", "\n", " self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)\n", " self.pool1 = nn.MaxPool2d(2, 2)\n", " self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)\n", " self.pool2 = nn.MaxPool2d(2, 2)\n", " self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)\n", " self.pool3 = nn.MaxPool2d(2, 2)\n", " self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=1)\n", " self.pool4 = nn.MaxPool2d(2, 2)\n", "\n", " self.fc1 = nn.Linear(256 * 14 * 14, 512)\n", " self.fc2 = nn.Linear(512, 3) # ¡CORREGIDO! 3 clases según tu set de datos\n", "\n", " def forward(self, x):\n", " x = F.relu(self.conv1(x))\n", " x = self.pool1(x)\n", " x = F.relu(self.conv2(x))\n", " x = self.pool2(x)\n", " x = F.relu(self.conv3(x))\n", " x = self.pool3(x)\n", " x = F.relu(self.conv4(x))\n", " x = self.pool4(x)\n", " \n", " x = x.view(x.size(0), -1)\n", " \n", " x = F.relu(self.fc1(x))\n", " x = self.fc2(x)\n", " \n", " return x " ] }, { "cell_type": "markdown", "id": "4d89a288-d1d9-45c9-b531-e752f05edf88", "metadata": {}, "source": [ "# Entrenamiento y Validación" ] }, { "cell_type": "code", "execution_count": 52, "id": "51914f4e-81f2-4d40-8936-913882ac9d55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch [1/25], Loss: 1.0685, Accuracy Validación: 59.54%\n", "Epoch [2/25], Loss: 0.8930, Accuracy Validación: 65.27%\n", "Epoch [3/25], Loss: 0.8210, Accuracy Validación: 74.64%\n", "Epoch [4/25], Loss: 0.7496, Accuracy Validación: 77.73%\n", "Epoch [5/25], Loss: 0.6877, Accuracy Validación: 75.08%\n", "Epoch [6/25], Loss: 0.6378, Accuracy Validación: 81.48%\n", "Epoch [7/25], Loss: 0.5746, Accuracy Validación: 84.23%\n", "Epoch [8/25], Loss: 0.4928, Accuracy Validación: 88.53%\n", "Epoch [9/25], Loss: 0.4882, Accuracy Validación: 88.64%\n", "Epoch [10/25], Loss: 0.4549, Accuracy Validación: 88.53%\n", "Epoch [11/25], Loss: 0.4393, Accuracy Validación: 89.97%\n", "Epoch [12/25], Loss: 0.4454, Accuracy Validación: 89.97%\n", "Epoch [13/25], Loss: 0.4319, Accuracy Validación: 89.97%\n", "Epoch [14/25], Loss: 0.4285, Accuracy Validación: 87.21%\n", "Epoch [15/25], Loss: 0.4126, Accuracy Validación: 91.18%\n", "Epoch [16/25], Loss: 0.4224, Accuracy Validación: 90.74%\n", "Epoch [17/25], Loss: 0.3957, Accuracy Validación: 90.19%\n", "Epoch [18/25], Loss: 0.4049, Accuracy Validación: 91.40%\n", "Epoch [19/25], Loss: 0.4042, Accuracy Validación: 90.85%\n", "Epoch [20/25], Loss: 0.4117, Accuracy Validación: 91.18%\n", "Epoch [21/25], Loss: 0.4091, Accuracy Validación: 90.96%\n", "Epoch [22/25], Loss: 0.3950, Accuracy Validación: 91.07%\n", "Epoch [23/25], Loss: 0.4002, Accuracy Validación: 90.96%\n", "Epoch [24/25], Loss: 0.4140, Accuracy Validación: 90.74%\n", "Epoch [25/25], Loss: 0.4075, Accuracy Validación: 90.63%\n" ] } ], "source": [ "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model = SimpleCNN().to(device)\n", "\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = optim.Adam(model.parameters(), lr=0.001)\n", "scheduler = StepLR(optimizer, step_size=7, gamma=0.1)\n", "num_epochs = 25\n", "\n", "for epoch in range(num_epochs):\n", " model.train()\n", " running_loss = 0.0\n", " for images, labels in train_loader:\n", " images, labels = images.to(device), labels.to(device)\n", " optimizer.zero_grad()\n", " outputs = model(images)\n", " loss = criterion(outputs, labels)\n", " loss.backward()\n", " optimizer.step()\n", " running_loss += loss.item()\n", "\n", " # FASE DE VALIDACIÓN (CORREGIDO)\n", " model.eval()\n", " correct_val = 0\n", " total_val = 0\n", " with torch.no_grad():\n", " for images, labels in val_loader:\n", " images, labels = images.to(device), labels.to(device)\n", " outputs = model(images)\n", " _, predicted = torch.max(outputs.data, 1)\n", " total_val += labels.size(0)\n", " correct_val += (predicted == labels).sum().item()\n", "\n", " val_accuracy = 100 * correct_val / total_val\n", " print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {running_loss/len(train_loader):.4f}, Accuracy Validación: {val_accuracy:.2f}%')\n", " \n", " # Llama al scheduler al final de la época\n", " scheduler.step()\n" ] }, { "cell_type": "markdown", "id": "15d5c9b5-de8d-4033-94ad-9138687668c9", "metadata": {}, "source": [ "# Evaluar con conjunto de validación" ] }, { "cell_type": "code", "execution_count": 18, "id": "eacf1abd-ab70-4dee-8506-074183528848", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Predicción: 0, Real: 2\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "mean = torch.tensor([0.485, 0.456, 0.406])\n", "std = torch.tensor([0.229, 0.224, 0.225])\n", "\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model.to(device)\n", "\n", "imagen_a_predecir_idx = 838 # índice válido de 0 a 911\n", "\n", "# Verificar si el índice está dentro del rango\n", "if imagen_a_predecir_idx >= len(test_dataset):\n", " print(f\"Error: El índice {imagen_a_predecir_idx} está fuera del rango del conjunto de test, que tiene {len(test_dataset)} imágenes.\")\n", "else:\n", " # Obtener la imagen y la etiqueta\n", " img_tuple = test_dataset[imagen_a_predecir_idx]\n", " img = img_tuple[0]\n", " label = img_tuple[1]\n", "\n", " # Mover la imagen a la GPU y añadir una dimensión de lote\n", " img = img.to(device)\n", " img_with_batch = img.unsqueeze(0)\n", "\n", " # Poner el modelo en modo de evaluación\n", " model.eval()\n", "\n", " # Realizar la predicción\n", " with torch.no_grad():\n", " output = model(img_with_batch)\n", " _, pred = torch.max(output, 1)\n", "\n", " print(f\"Predicción: {pred.item()}, Real: {label}\")\n", "\n", " # Desnormalizar la imagen para su visualización\n", " img_desnormalizada = img.cpu() * std.view(3, 1, 1) + mean.view(3, 1, 1)\n", " \n", " # Recortar los valores para asegurar que estén en [0, 1]\n", " img_desnormalizada = torch.clamp(img_desnormalizada, 0, 1)\n", "\n", " # Mostrar la imagen\n", " plt.imshow(img_desnormalizada.permute(1, 2, 0))\n", " plt.title(f\"Índice de la imagen: {imagen_a_predecir_idx}\")\n", " plt.show()" ] }, { "cell_type": "markdown", "id": "1845210e-cf5b-4a49-9564-33865ebb16ee", "metadata": {}, "source": [ "# Accuracy final con datos de Test" ] }, { "cell_type": "code", "execution_count": 55, "id": "a69f4a5f-c0ff-4f41-bb24-488fdde4252c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Precisión del modelo en las 912 imágenes de test: 91.23%\n" ] } ], "source": [ "import torch\n", "\n", "# Asegúrate de que el modelo ya está entrenado\n", "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "model.to(device)\n", "\n", "# Poner el modelo en modo de evaluación\n", "model.eval()\n", "\n", "correct = 0\n", "total = 0\n", "\n", "# Desactivar el cálculo de gradientes\n", "with torch.no_grad():\n", " # Iterar sobre el DataLoader del conjunto de test\n", " for images, labels in test_loader:\n", " images, labels = images.to(device), labels.to(device)\n", " \n", " # Realizar la predicción\n", " outputs = model(images)\n", " _, predicted = torch.max(outputs.data, 1)\n", " \n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", "final_accuracy = 100 * correct / total\n", "print(f'Precisión del modelo en las {total} imágenes de test: {final_accuracy:.2f}%')" ] }, { "cell_type": "markdown", "id": "c681ff4e-7e7a-4b88-bed7-81025ed694c6", "metadata": {}, "source": [ "# Guardar Modelo" ] }, { "cell_type": "code", "execution_count": 56, "id": "3aaec33b-a6c4-41b3-b8a9-68cca9201516", "metadata": {}, "outputs": [], "source": [ "model_name = \"torch_brain_cancer\"\n", "torch.save(model.state_dict(), model_name)" ] }, { "cell_type": "markdown", "id": "b4273ab1-1de3-4db1-b8c9-0af17ff2aacb", "metadata": {}, "source": [ "### Cargar el modelo (only weights)" ] }, { "cell_type": "code", "execution_count": 8, "id": "960d32c8-8676-4e3f-a7b8-c0cfb6045df9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = SimpleCNN()\n", "model.load_state_dict(torch.load('modelo/torch_brain_cancer_91', weights_only=True)) # Cargar presos del modelo " ] }, { "cell_type": "markdown", "id": "c882c9da-414a-4273-9676-c13765577531", "metadata": {}, "source": [ "# Conclusión Modelo\n", "### ¿Qué predice el modelo?\n", "El modelo no predice simplemente \"cáncer\" o \"no cáncer\" con una respuesta binaria. En su lugar, es un clasificador multiclase que predice a cuál de las 4 clases mencionadas anteriormente pertenece una imagen.\n", "\n", "- Si predice la Clase 0, significa que ha identificado un tumor de tipo glioma.\n", "\n", "- Si predice la Clase 1, significa que ha identificado un tumor de tipo meningioma.\n", "\n", "- Si predice la Clase 2, significa que la imagen no tiene un tumor (no_tumor).\n", "\n", "- Si predice la Clase 3, significa que ha identificado un tumor de tipo pituitary." ] } ], "metadata": { "kernelspec": { "display_name": "Deep Learning Kernel", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 5 }