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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"import os\n",
"from tensorflow.keras import layers, models\n",
"from tensorflow.keras.optimizers import Adam\n",
"import numpy as np\n",
"from sklearn.metrics import f1_score, precision_score, recall_score, confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 32 images belonging to 4 classes.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 8 images belonging to 4 classes.\n",
"Class indices: {'Cyst': 0, 'Normal': 1, 'Stone': 2, 'Tumor': 3}\n",
"done\n"
]
}
],
"source": [
"# Define paths for the dataset\n",
"base_dir = '../images'\n",
"\n",
"# Create ImageDataGenerators for training, validation, and testing\n",
"data_gen = ImageDataGenerator(\n",
" rescale=1.0/255, # Normalize pixel values\n",
" validation_split=0.2 # Split for validation\n",
")\n",
"\n",
"# Load training data\n",
"train_data = data_gen.flow_from_directory(\n",
" base_dir,\n",
" target_size=(150, 150),\n",
" batch_size=2,\n",
" class_mode='categorical',\n",
" subset='training'\n",
")\n",
"\n",
"# Load validation data\n",
"val_data = data_gen.flow_from_directory(\n",
" base_dir,\n",
" target_size=(150, 150),\n",
" batch_size=2,\n",
" class_mode='categorical',\n",
" subset='validation'\n",
")\n",
"\n",
"# Print class indices\n",
"print(\"Class indices:\", train_data.class_indices)\n",
"print('done')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"input_shape = (150, 150, 3) # 750x750 RGB images\n",
"num_classes = 4"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [],
"source": [
"# Create the CNN model\n",
"model = models.Sequential([\n",
" # Input layer\n",
" layers.Input(shape=input_shape),\n",
" \n",
" # First Convolutional Block\n",
" layers.Conv2D(32, kernel_size=(3, 3), activation='relu', padding='same'),\n",
" layers.BatchNormalization(),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # Second Convolutional Block\n",
" layers.Conv2D(64, kernel_size=(3, 3), activation='relu', padding='same'),\n",
" layers.BatchNormalization(),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # Third Convolutional Block\n",
" layers.Conv2D(128, kernel_size=(3, 3), activation='relu', padding='same'),\n",
" layers.BatchNormalization(),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # Fourth Convolutional Block\n",
" layers.Conv2D(256, kernel_size=(3, 3), activation='relu', padding='same'),\n",
" layers.BatchNormalization(),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # Fifth Convolutional Block\n",
" layers.Conv2D(512, kernel_size=(3, 3), activation='relu', padding='same'),\n",
" layers.BatchNormalization(),\n",
" layers.MaxPooling2D(pool_size=(2, 2)),\n",
" \n",
" # Fully Connected Layers\n",
" layers.Flatten(),\n",
" # layers.Dense(1024, activation='relu'), # Adjusted to match the input shape\n",
" # layers.Dropout(0.5),\n",
" # layers.Dense(128, activation='relu'),\n",
" # layers.Dropout(0.5),\n",
" # \n",
" # Output Layer\n",
" layers.Dense(num_classes, activation='softmax')\n",
"])\n",
"\n",
"# Compile the model\n",
"model.compile(optimizer=Adam(learning_rate=0.00001),\n",
" loss='categorical_crossentropy', # Use 'categorical_crossentropy' for one-hot encoded labels\n",
" metrics=['accuracy', 'f1_score'])\n"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 158ms/step - accuracy: 0.5123 - f1_score: 0.4374 - loss: 1.7693 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.3858\n",
"Epoch 2/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 127ms/step - accuracy: 0.9428 - f1_score: 0.8688 - loss: 0.0977 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.3971\n",
"Epoch 3/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 0.0092 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4072\n",
"Epoch 4/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - accuracy: 1.0000 - f1_score: 0.9265 - loss: 0.0102 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4199\n",
"Epoch 5/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 0.0051 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4359\n",
"Epoch 6/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - accuracy: 1.0000 - f1_score: 0.9118 - loss: 0.0038 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4536\n",
"Epoch 7/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 130ms/step - accuracy: 1.0000 - f1_score: 0.9265 - loss: 0.0032 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4734\n",
"Epoch 8/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 1.0000 - f1_score: 0.8824 - loss: 0.0044 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.4929\n",
"Epoch 9/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 138ms/step - accuracy: 1.0000 - f1_score: 0.9265 - loss: 0.0031 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.5133\n",
"Epoch 10/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 129ms/step - accuracy: 1.0000 - f1_score: 0.9706 - loss: 0.0024 - val_accuracy: 0.2500 - val_f1_score: 0.1000 - val_loss: 1.5346\n",
"Epoch 11/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 127ms/step - accuracy: 1.0000 - f1_score: 0.8971 - loss: 0.0029 - val_accuracy: 0.2500 - val_f1_score: 0.1111 - val_loss: 1.5535\n",
"Epoch 12/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 0.0029 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5660\n",
"Epoch 13/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - accuracy: 1.0000 - f1_score: 0.9118 - loss: 0.0014 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5759\n",
"Epoch 14/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 129ms/step - accuracy: 1.0000 - f1_score: 0.8824 - loss: 0.0016 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5809\n",
"Epoch 15/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 0.0014 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5782\n",
"Epoch 16/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 0.0011 - val_accuracy: 0.2500 - val_f1_score: 0.1250 - val_loss: 1.5731\n",
"Epoch 17/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 0.0015 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5475\n",
"Epoch 18/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 130ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 0.0010 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.5120\n",
"Epoch 19/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 137ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 0.0010 - val_accuracy: 0.2500 - val_f1_score: 0.1250 - val_loss: 1.4640\n",
"Epoch 20/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 142ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 0.0013 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.4229\n",
"Epoch 21/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.9118 - loss: 9.3909e-04 - val_accuracy: 0.3750 - val_f1_score: 0.2429 - val_loss: 1.3761\n",
"Epoch 22/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 1.0000 - f1_score: 0.9118 - loss: 8.4527e-04 - val_accuracy: 0.5000 - val_f1_score: 0.3333 - val_loss: 1.3416\n",
"Epoch 23/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 8.5071e-04 - val_accuracy: 0.5000 - val_f1_score: 0.3333 - val_loss: 1.3063\n",
"Epoch 24/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 7.2937e-04 - val_accuracy: 0.5000 - val_f1_score: 0.3429 - val_loss: 1.2619\n",
"Epoch 25/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.9265 - loss: 6.2996e-04 - val_accuracy: 0.5000 - val_f1_score: 0.3429 - val_loss: 1.1914\n",
"Epoch 26/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.9265 - loss: 7.7824e-04 - val_accuracy: 0.5000 - val_f1_score: 0.3429 - val_loss: 1.1314\n",
"Epoch 27/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.8971 - loss: 6.3872e-04 - val_accuracy: 0.6250 - val_f1_score: 0.5333 - val_loss: 1.0617\n",
"Epoch 28/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 6.7060e-04 - val_accuracy: 0.7500 - val_f1_score: 0.7333 - val_loss: 1.0080\n",
"Epoch 29/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - accuracy: 1.0000 - f1_score: 0.9559 - loss: 6.5673e-04 - val_accuracy: 0.7500 - val_f1_score: 0.7333 - val_loss: 0.9536\n",
"Epoch 30/30\n",
"\u001b[1m16/16\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 1.0000 - f1_score: 0.9412 - loss: 7.8768e-04 - val_accuracy: 0.7500 - val_f1_score: 0.7333 - val_loss: 0.9146\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Model saved as medical_classifier.h5\n"
]
}
],
"source": [
"# Train the model\n",
"history = model.fit(\n",
" train_data,\n",
" steps_per_epoch=len(train_data),\n",
" epochs=30,\n",
" validation_data=val_data,\n",
" validation_steps=len(val_data)\n",
")\n",
"\n",
"# Save the model\n",
"model.save('medical_classifier.h5')\n",
"print(\"Model saved as medical_classifier.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 32ms/step\n",
"F1 Score on validation data: 0.35\n"
]
}
],
"source": [
"\n",
"val_data.reset()\n",
"predictions = model.predict(val_data, steps=len(val_data), verbose=1)\n",
"y_pred = np.argmax(predictions, axis=1)\n",
"y_true = val_data.classes\n",
"f1 = f1_score(y_true, y_pred, average='weighted')\n",
"print(\"F1 Score on validation data:\", f1)"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 25ms/step\n",
"F1 Score on validation data: 0.3666666666666667\n"
]
}
],
"source": [
"from sklearn.metrics import f1_score\n",
"import numpy as np\n",
"from tensorflow.keras.preprocessing import image\n",
"\n",
"# Calculate F1 score on validation data\n",
"val_data.reset()\n",
"predictions = model.predict(val_data, steps=len(val_data), verbose=1)\n",
"y_pred = np.argmax(predictions, axis=1)\n",
"y_true = val_data.classes\n",
"f1 = f1_score(y_true, y_pred, average='weighted')\n",
"print(\"F1 Score on validation data:\", f1)\n",
"\n",
"# Test the model on a random image\n",
"def test_random_image(img_path):\n",
" img = image.load_img(img_path, target_size=(150, 150))\n",
" img_array = image.img_to_array(img)\n",
" img_array = np.expand_dims(img_array, axis=0)\n",
" img_array /= 255.0\n",
"\n",
" prediction = model.predict(img_array)\n",
" predicted_class = np.argmax(prediction, axis=1)\n",
" class_indices = {v: k for k, v in train_data.class_indices.items()}\n",
" predicted_label = class_indices[predicted_class[0]]\n",
"\n",
" print(f\"Predicted class: {predicted_label}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m4/4\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n",
"F1 Score on validation data: 0.3666666666666667\n",
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 48ms/step\n",
"Predicted class: Cyst\n"
]
}
],
"source": [
"val_data.reset()\n",
"predictions = model.predict(val_data, steps=len(val_data), verbose=1)\n",
"y_pred = np.argmax(predictions, axis=1)\n",
"y_true = val_data.classes\n",
"f1 = f1_score(y_true, y_pred, average='weighted')\n",
"print(\"F1 Score on validation data:\", f1)\n",
"random_image_path = os.path.join(base_dir, 'test', 'Cyst', 'Cyst- (18).jpg') # Replace 'class_name' and 'random_image.jpg' with actual values\n",
"test_random_image(random_image_path)"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n",
"Predicted class: Normal\n"
]
}
],
"source": [
"random_image_path = os.path.join(base_dir, 'test', 'Normal', 'Normal- (286).jpg') # Replace 'class_name' and 'random_image.jpg' with actual values\n",
"test_random_image(random_image_path)"
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 51ms/step\n",
"Predicted class: Stone\n"
]
}
],
"source": [
"random_image_path = os.path.join(base_dir, 'test', 'Stone', 'Stone- (62).jpg') # Replace 'class_name' and 'random_image.jpg' with actual values\n",
"test_random_image(random_image_path)"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 56ms/step\n",
"Predicted class: Tumor\n"
]
}
],
"source": [
"random_image_path = os.path.join(base_dir, 'test', 'Tumor', 'Tumor- (54).jpg') # Replace 'class_name' and 'random_image.jpg' with actual values\n",
"test_random_image(random_image_path)"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 55ms/step\n",
"Predicted class: Tumor\n"
]
}
],
"source": [
"r_img_path = os.path.join(base_dir, 'test', 'Tumor', 'Tumor- (44).jpg') # Replace 'class_name' and 'random_image.jpg' with actual values\n",
"import_model = tf.keras.models.load_model('./medical_classifier.h5')\n",
"img = image.load_img(r_img_path, target_size=(150, 150))\n",
"img_array = image.img_to_array(img)\n",
"img_array = np.expand_dims(img_array, axis=0)\n",
"img_array /= 255.0\n",
"\n",
"prediction = model.predict(img_array)\n",
"predicted_class = np.argmax(prediction, axis=1)\n",
"class_indices = {v: k for k, v in train_data.class_indices.items()}\n",
"predicted_label = class_indices[predicted_class[0]]\n",
"\n",
"print(f\"Predicted class: {predicted_label}\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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