Skin Cancer Detection.ipynb

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    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from glob import glob\n",
    "from PIL import Image\n",
    "import cv2\n",
    "from sklearn.model_selection import train_test_split\n",
    "import tensorflow as tf\n",
    "import warnings\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.io import read_file\n",
    "from tensorflow.image import decode_jpeg, resize\n",
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    "path = \"../data/train/*/*.jpg\"\n",
    "images = glob(path)\n",
    "len(images)"
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    "df = pd.DataFrame({\"filepath\": images})\n",
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       "benign       1440\n",
       "malignant    1197\n",
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   ],
   "source": [
    "labels = df[\"label\"].value_counts()\n",
    "\n",
    "values = labels.values\n",
    "names = labels.index\n",
    "\n",
    "fig = px.pie(df, values=values, names=names, hole=0.5)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8aeb5a22",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[\"filepath\"]\n",
    "y = df[\"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "50a3d5fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_image(filepath, label=None):\n",
    "    image = read_file(filepath)\n",
    "    image = decode_jpeg(image)\n",
    "    image = resize(image, [224, 224])\n",
    "    image = tf.cast(image, tf.float32) / 255.0\n",
    "    \n",
    "    if label == \"benign\":\n",
    "        Label = 0\n",
    "    else:\n",
    "        Label = 1\n",
    "        \n",
    "    return image, Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d081e4bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4242)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2d75224d",
   "metadata": {},
   "outputs": [],
   "source": [
    "AUTO = tf.data.experimental.AUTOTUNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cb82964b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = (\n",
    "    tf.data.Dataset\n",
    "    .from_tensor_slices((X_train, y_train))\n",
    "    .map(decode_image, num_parallel_calls=AUTO)\n",
    "    .batch(32)\n",
    "    .prefetch(AUTO)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0f51be8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ds = (\n",
    "    tf.data.Dataset\n",
    "    .from_tensor_slices((X_train, y_train))\n",
    "    .map(decode_image, num_parallel_calls=AUTO)\n",
    "    .batch(32)\n",
    "    .prefetch(AUTO)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e2e026aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential([\n",
    "    Conv2D(32, (3,3), activation=\"relu\", input_shape=(224, 224, 3)),\n",
    "    MaxPooling2D((2,2)),\n",
    "    \n",
    "    Conv2D(64, (3,3), activation=\"relu\"),\n",
    "    MaxPooling2D((2,2)),\n",
    "    \n",
    "    Conv2D(128, (3,3), activation=\"relu\"),\n",
    "    MaxPooling2D((2,2)),\n",
    "    \n",
    "    Flatten(),\n",
    "    \n",
    "    Dense(64, activation=\"relu\"),\n",
    "    Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "model.compile(optimizer=\"adam\", loss=\"binary_crossentropy\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b7601410",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-28 22:48:06.644813: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [2109]\n",
      "\t [[{{node Placeholder/_1}}]]\n",
      "2023-05-28 22:48:06.645799: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [2109]\n",
      "\t [[{{node Placeholder/_1}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66/66 [==============================] - ETA: 0s - loss: 0.6265 - accuracy: 0.6752"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-05-28 22:48:56.212071: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [2109]\n",
      "\t [[{{node Placeholder/_1}}]]\n",
      "2023-05-28 22:48:56.212441: I tensorflow/core/common_runtime/executor.cc:1197] [/device:CPU:0] (DEBUG INFO) Executor start aborting (this does not indicate an error and you can ignore this message): INVALID_ARGUMENT: You must feed a value for placeholder tensor 'Placeholder/_1' with dtype string and shape [2109]\n",
      "\t [[{{node Placeholder/_1}}]]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "66/66 [==============================] - 66s 993ms/step - loss: 0.6265 - accuracy: 0.6752 - val_loss: 0.5155 - val_accuracy: 0.7572\n",
      "Epoch 2/10\n",
      "66/66 [==============================] - 77s 1s/step - loss: 0.4623 - accuracy: 0.7667 - val_loss: 0.4116 - val_accuracy: 0.7994\n",
      "Epoch 3/10\n",
      "66/66 [==============================] - 73s 1s/step - loss: 0.4202 - accuracy: 0.7866 - val_loss: 0.3631 - val_accuracy: 0.8127\n",
      "Epoch 4/10\n",
      "66/66 [==============================] - 72s 1s/step - loss: 0.3824 - accuracy: 0.7975 - val_loss: 0.3392 - val_accuracy: 0.8260\n",
      "Epoch 5/10\n",
      "66/66 [==============================] - 68s 1s/step - loss: 0.3483 - accuracy: 0.8255 - val_loss: 0.3144 - val_accuracy: 0.8393\n",
      "Epoch 6/10\n",
      "66/66 [==============================] - 71s 1s/step - loss: 0.3261 - accuracy: 0.8288 - val_loss: 0.3004 - val_accuracy: 0.8511\n",
      "Epoch 7/10\n",
      "66/66 [==============================] - 72s 1s/step - loss: 0.3344 - accuracy: 0.8269 - val_loss: 0.2773 - val_accuracy: 0.8630\n",
      "Epoch 8/10\n",
      "66/66 [==============================] - 72s 1s/step - loss: 0.2906 - accuracy: 0.8578 - val_loss: 0.2742 - val_accuracy: 0.8625\n",
      "Epoch 9/10\n",
      "66/66 [==============================] - 74s 1s/step - loss: 0.2840 - accuracy: 0.8653 - val_loss: 0.2660 - val_accuracy: 0.8824\n",
      "Epoch 10/10\n",
      "66/66 [==============================] - 66s 999ms/step - loss: 0.2810 - accuracy: 0.8663 - val_loss: 0.2620 - val_accuracy: 0.8843\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_ds, validation_data=test_ds, epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e3ac37d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>val_loss</th>\n",
       "      <th>val_accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.626508</td>\n",
       "      <td>0.675202</td>\n",
       "      <td>0.515491</td>\n",
       "      <td>0.757231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.462346</td>\n",
       "      <td>0.766714</td>\n",
       "      <td>0.411628</td>\n",
       "      <td>0.799431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.420226</td>\n",
       "      <td>0.786629</td>\n",
       "      <td>0.363105</td>\n",
       "      <td>0.812707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.382448</td>\n",
       "      <td>0.797534</td>\n",
       "      <td>0.339240</td>\n",
       "      <td>0.825984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.348298</td>\n",
       "      <td>0.825510</td>\n",
       "      <td>0.314413</td>\n",
       "      <td>0.839260</td>\n",
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      "text/plain": [
       "       loss  accuracy  val_loss  val_accuracy\n",
       "0  0.626508  0.675202  0.515491      0.757231\n",
       "1  0.462346  0.766714  0.411628      0.799431\n",
       "2  0.420226  0.786629  0.363105      0.812707\n",
       "3  0.382448  0.797534  0.339240      0.825984\n",
       "4  0.348298  0.825510  0.314413      0.839260"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hist_df = pd.DataFrame(history.history)\n",
    "hist_df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 35,
   "id": "8c958d50",
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"accuracy\"])\n",
    "plt.plot(history.history[\"val_accuracy\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Accuracy\")\n",
    "plt.title(\"Model Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bea367d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(history.history[\"loss\"])\n",
    "plt.plot(history.history[\"val_loss\"])\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.title(\"Model Loss\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6f0c94aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"skin.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b83ca7a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}