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{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Brain Tumor MRI Detection"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"pip install tensorflow"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"from tensorflow.keras.models import load_model\n",
"from tensorflow.keras.preprocessing import image\n",
"import os\n",
"import numpy as np\n",
"from PIL import Image"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Current Directory: e:\\Github Projects\\BrainTumorMRIDetection\n"
]
}
],
"source": [
"current_dir = os.getcwd()\n",
"print (\"Current Directory: \" + current_dir)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"train_dir = os.path.join(current_dir, 'Testing')\n",
"val_dir = os.path.join(current_dir, 'Training')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"# Define the target size and batch size\n",
"target_size = (1250, 1250)\n",
"batch_size = 32\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 394 images belonging to 4 classes.\n",
"Found 2870 images belonging to 4 classes.\n"
]
}
],
"source": [
"# Define the training and validation data generators\n",
"train_datagen = ImageDataGenerator(rescale=1./255)\n",
"train_generator = train_datagen.flow_from_directory(\n",
" train_dir,\n",
" target_size=target_size,\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n",
"\n",
"val_datagen = ImageDataGenerator(rescale=1./255)\n",
"val_generator = val_datagen.flow_from_directory(\n",
" val_dir,\n",
" target_size=target_size,\n",
" batch_size=batch_size,\n",
" class_mode='categorical')\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"# Define the model\n",
"model = tf.keras.models.Sequential([\n",
" tf.keras.layers.Conv2D(32, (3, 3), activation='relu',\n",
" input_shape=(target_size[0], target_size[1], 3)),\n",
" tf.keras.layers.MaxPooling2D((2, 2)),\n",
" tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),\n",
" tf.keras.layers.MaxPooling2D((2, 2)),\n",
" tf.keras.layers.Conv2D(128, (3, 3), activation='relu'),\n",
" tf.keras.layers.MaxPooling2D((2, 2)),\n",
" tf.keras.layers.Flatten(),\n",
" tf.keras.layers.Dense(128, activation='relu'),\n",
" tf.keras.layers.Dropout(0.5),\n",
" tf.keras.layers.Dense(train_generator.num_classes, activation='softmax')\n",
"])\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# Compile the model\n",
"model.compile(optimizer='adam',\n",
" loss='categorical_crossentropy',\n",
" metrics=['accuracy'])\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/15\n",
"12/12 [==============================] - 753s 64s/step - loss: 17.4665 - accuracy: 0.2431 - val_loss: 1.4717 - val_accuracy: 0.2883\n",
"Epoch 2/15\n",
"12/12 [==============================] - 694s 60s/step - loss: 1.1907 - accuracy: 0.4779 - val_loss: 1.4602 - val_accuracy: 0.2798\n",
"Epoch 3/15\n",
"12/12 [==============================] - 704s 61s/step - loss: 0.8829 - accuracy: 0.6575 - val_loss: 1.5343 - val_accuracy: 0.2791\n",
"Epoch 4/15\n",
"12/12 [==============================] - 697s 61s/step - loss: 0.4633 - accuracy: 0.8398 - val_loss: 1.7458 - val_accuracy: 0.3206\n",
"Epoch 5/15\n",
"12/12 [==============================] - 690s 60s/step - loss: 0.2428 - accuracy: 0.9309 - val_loss: 2.3506 - val_accuracy: 0.3536\n",
"Epoch 6/15\n",
"12/12 [==============================] - 698s 61s/step - loss: 0.1575 - accuracy: 0.9558 - val_loss: 2.2596 - val_accuracy: 0.3588\n",
"Epoch 7/15\n",
"12/12 [==============================] - 694s 61s/step - loss: 0.1069 - accuracy: 0.9696 - val_loss: 1.9421 - val_accuracy: 0.3272\n",
"Epoch 8/15\n",
"12/12 [==============================] - 694s 61s/step - loss: 0.0688 - accuracy: 0.9807 - val_loss: 3.2596 - val_accuracy: 0.3711\n",
"Epoch 9/15\n",
"12/12 [==============================] - 685s 61s/step - loss: 0.1024 - accuracy: 0.9696 - val_loss: 2.0157 - val_accuracy: 0.3722\n",
"Epoch 10/15\n",
"12/12 [==============================] - 699s 61s/step - loss: 0.0556 - accuracy: 0.9890 - val_loss: 2.7399 - val_accuracy: 0.3430\n",
"Epoch 11/15\n",
"12/12 [==============================] - 696s 61s/step - loss: 0.0561 - accuracy: 0.9862 - val_loss: 2.4300 - val_accuracy: 0.3831\n",
"Epoch 12/15\n",
"12/12 [==============================] - 684s 60s/step - loss: 0.0320 - accuracy: 0.9917 - val_loss: 2.5653 - val_accuracy: 0.3511\n",
"Epoch 13/15\n",
"12/12 [==============================] - 681s 61s/step - loss: 0.0493 - accuracy: 0.9890 - val_loss: 2.8736 - val_accuracy: 0.3515\n",
"Epoch 14/15\n",
"12/12 [==============================] - 689s 60s/step - loss: 0.0213 - accuracy: 0.9917 - val_loss: 3.0044 - val_accuracy: 0.3704\n",
"Epoch 15/15\n",
"12/12 [==============================] - 692s 62s/step - loss: 0.0407 - accuracy: 0.9917 - val_loss: 2.8754 - val_accuracy: 0.3838\n"
]
}
],
"source": [
"# Train the model\n",
"history = model.fit(\n",
" train_generator,\n",
" steps_per_epoch=train_generator.samples//batch_size,\n",
" epochs=15,\n",
" validation_data=val_generator,\n",
" validation_steps=val_generator.samples//batch_size)"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"Saving The Model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"# Save the trained model in the current directory\n",
"model.save(os.path.join(current_dir, 'model.h5'))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.8.8"
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"orig_nbformat": 4
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"nbformat": 4,
"nbformat_minor": 2
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