Skin Cancer data preparation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e2cde631",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4ff3268",
   "metadata": {},
   "outputs": [],
   "source": [
    "df =pd.read_csv('ISIC_2019_Training_GroundTruth.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d0941b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>image</th>\n",
       "      <th>MEL</th>\n",
       "      <th>NV</th>\n",
       "      <th>BCC</th>\n",
       "      <th>AK</th>\n",
       "      <th>BKL</th>\n",
       "      <th>DF</th>\n",
       "      <th>VASC</th>\n",
       "      <th>SCC</th>\n",
       "      <th>UNK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ISIC_0000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ISIC_0000001</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ISIC_0000002</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ISIC_0000003</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ISIC_0000004</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          image  MEL   NV  BCC   AK  BKL   DF  VASC  SCC  UNK\n",
       "0  ISIC_0000000  0.0  1.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0\n",
       "1  ISIC_0000001  0.0  1.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0\n",
       "2  ISIC_0000002  1.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0\n",
       "3  ISIC_0000003  0.0  1.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0\n",
       "4  ISIC_0000004  1.0  0.0  0.0  0.0  0.0  0.0   0.0  0.0  0.0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fce07aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4522"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mel_list=df[df['MEL']==1.0]['image'].tolist()\n",
    "len(mel_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cba200d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "import os\n",
    "import shutil\n",
    "  \n",
    "source = 'D:/ISIC_2019_Training_Input/ISIC_2019_Training_Input/'\n",
    "destination = 'D:/ISIC_2019_Training_Input/Melanoma/'\n",
    "  \n",
    "allfiles = os.listdir(source)\n",
    "  \n",
    "for f in allfiles:\n",
    "    m=f.split('.')[0]\n",
    "    if m in mel_list:\n",
    "        file=\"melanoma_\" + str(f)\n",
    "        print(file)\n",
    "        shutil.copy(source + f, destination + file)\n",
    "        \n",
    "#     file=\"thorsten_Neutral_\" + str(f)\n",
    "#     shutil.copy(source + f, destination + file)\n",
    " #   print(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8bdf6fd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4600, 10)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfnv = df[df['NV']==1.0]\n",
    "dfnv=dfnv.head(4600)\n",
    "dfnv.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d548fcd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4600"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nv_list=dfnv['image'].tolist()\n",
    "len(nv_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f85a9eb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nv_ISIC_0000000.jpg\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nv_ISIC_0028144.jpg\n",
      "nv_ISIC_0028145.jpg\n",
      "nv_ISIC_0028149.jpg\n",
      "nv_ISIC_0028150.jpg\n",
      "nv_ISIC_0028151.jpg\n",
      "nv_ISIC_0028152.jpg\n"
     ]
    }
   ],
   "source": [
    "source = 'D:/ISIC_2019_Training_Input/ISIC_2019_Training_Input/'\n",
    "destination = 'D:/ISIC_2019_Training_Input/Nevus/'\n",
    "  \n",
    "allfiles = os.listdir(source)\n",
    "  \n",
    "for f in allfiles:\n",
    "    m=f.split('.')[0]\n",
    "    if m in nv_list:\n",
    "        file=\"nv_\" + str(f)\n",
    "        print(file)\n",
    "        shutil.copy(source + f, destination + file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3f441848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3323, 10)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfbc = df[df['BCC']==1.0]\n",
    "\n",
    "dfbc.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "25b3726c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3323"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bc_list=dfbc['image'].tolist()\n",
    "len(bc_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ed2caf1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "source = 'D:/ISIC_2019_Training_Input/ISIC_2019_Training_Input/'\n",
    "destination = 'D:/ISIC_2019_Training_Input/Basal_cell_carcinoma/'\n",
    "  \n",
    "allfiles = os.listdir(source)\n",
    "  \n",
    "for f in allfiles:\n",
    "    m=f.split('.')[0]\n",
    "    if m in bc_list:\n",
    "        file=\"bcc_\" + str(f)\n",
    "        \n",
    "        shutil.copy(source + f, destination + file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2bddd481",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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