From 541d3dc422c42133bd72525bc8fd255356fde162 Mon Sep 17 00:00:00 2001 From: Ojasvi Doye <123376306+ojasvidoye@users.noreply.github.com> Date: Wed, 31 Jan 2024 23:50:59 +0530 Subject: [PATCH] =?UTF-8?q?Adding=20more=20convolutional=20layers=20to=20t?= =?UTF-8?q?he=C2=A0model=20so=20that=20it=20can=20capture=20more=20complex?= =?UTF-8?q?=20features.?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Model/Brain_Tumor_detection.ipynb | 343 +++++++++--------- 1 file changed, 172 insertions(+), 171 deletions(-) diff --git a/Brain Tumor Detection/Model/Brain_Tumor_detection.ipynb b/Brain Tumor Detection/Model/Brain_Tumor_detection.ipynb index 9820e2ce5..f822bc432 100644 --- a/Brain Tumor Detection/Model/Brain_Tumor_detection.ipynb +++ b/Brain Tumor Detection/Model/Brain_Tumor_detection.ipynb @@ -1,22 +1,12 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "Brain_Tumor_detection.ipynb", - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, "cells": [ { "cell_type": "code", + "execution_count": 57, + "metadata": { + "id": "7xoyJ3f-62oo" + }, + "outputs": [], "source": [ "import os\n", "import keras \n", @@ -30,19 +20,11 @@ "import matplotlib.pyplot as plt\n", "import cv2\n", "from google.colab.patches import cv2_imshow" - ], - "metadata": { - "id": "7xoyJ3f-62oo" - }, - "execution_count": 57, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "encoder = OneHotEncoder()\n", - "encoder.fit([[0], [1]]) \n" - ], + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -50,34 +32,42 @@ "id": "oFOQaZ007s7x", "outputId": "fd469078-1ce6-44fa-f01e-8647f5b77b2f" }, - "execution_count": 3, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "OneHotEncoder()" ] }, + "execution_count": 3, "metadata": {}, - "execution_count": 3 + "output_type": "execute_result" } + ], + "source": [ + "encoder = OneHotEncoder()\n", + "encoder.fit([[0], [1]]) \n" ] }, { "cell_type": "code", - "source": [ - "data=[]\n", - "result=[]" - ], + "execution_count": 16, "metadata": { "id": "l1esay4q8tJa" }, - "execution_count": 16, - "outputs": [] + "outputs": [], + "source": [ + "data=[]\n", + "result=[]" + ] }, { "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "h_guzPqm8J6V" + }, + "outputs": [], "source": [ "basepath=\"/content/drive/MyDrive/JWOC/Brain_Tumor_detection/brain_tumor_dataset/yes\"\n", "for filename in os.listdir(basepath):\n", @@ -88,19 +78,11 @@ " if(img.shape == (128,128,3)):\n", " data.append(np.array(img))\n", " result.append(encoder.transform([[0]]).toarray())" - ], - "metadata": { - "id": "h_guzPqm8J6V" - }, - "execution_count": 17, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "data = np.array(data)\n", - "print(data.shape)" - ], + "execution_count": 12, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -108,33 +90,34 @@ "id": "95aXQrEr9Wyw", "outputId": "0d62ba8d-fcca-4505-c712-8394e631dbf0" }, - "execution_count": 12, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(138, 128, 128, 3)\n" ] } + ], + "source": [ + "data = np.array(data)\n", + "print(data.shape)" ] }, { "cell_type": "code", - "source": [ - "result = np.array(result)" - ], + "execution_count": 13, "metadata": { "id": "glbqkZN29aBy" }, - "execution_count": 13, - "outputs": [] + "outputs": [], + "source": [ + "result = np.array(result)" + ] }, { "cell_type": "code", - "source": [ - "print(result.shape)" - ], + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -142,19 +125,26 @@ "id": "O3CsHQrU9bsC", "outputId": "b2739dc3-603a-47ed-9e94-a60f0cb13af5" }, - "execution_count": 14, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(138, 1, 2)\n" ] } + ], + "source": [ + "print(result.shape)" ] }, { "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "bfOgD11P8yrE" + }, + "outputs": [], "source": [ "basepath=\"/content/drive/MyDrive/JWOC/Brain_Tumor_detection/brain_tumor_dataset/no\"\n", "for filename in os.listdir(basepath):\n", @@ -165,22 +155,11 @@ " if(img.shape == (128,128,3)):\n", " data.append(np.array(img))\n", " result.append(encoder.transform([[1]]).toarray())" - ], - "metadata": { - "id": "bfOgD11P8yrE" - }, - "execution_count": 18, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "data = np.array(data)\n", - "print(data.shape)\n", - "result = np.array(result)\n", - "result = result.reshape(217,2)\n", - "print(result.shape)" - ], + "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -188,67 +167,47 @@ "id": "IDHX4d9789-n", "outputId": "b2383da3-5d17-44f5-84cf-b7caf22b944b" }, - "execution_count": 20, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "(217, 128, 128, 3)\n", "(217, 2)\n" ] } + ], + "source": [ + "data = np.array(data)\n", + "print(data.shape)\n", + "result = np.array(result)\n", + "result = result.reshape(217,2)\n", + "print(result.shape)" ] }, { "cell_type": "code", - "source": [ - "x_train,x_test,y_train,y_test = train_test_split(data, result, test_size=0.2, shuffle=True, random_state=0)" - ], + "execution_count": 21, "metadata": { "id": "JZecjsGd9M2j" }, - "execution_count": 21, - "outputs": [] + "outputs": [], + "source": [ + "x_train,x_test,y_train,y_test = train_test_split(data, result, test_size=0.2, shuffle=True, random_state=0)" + ] }, { "cell_type": "markdown", - "source": [ - "architecture-1" - ], "metadata": { "id": "VgOhqOkcWLnK" - } + }, + "source": [ + "architecture-1" + ] }, { "cell_type": "code", - "source": [ - "model = Sequential()\n", - "\n", - "model.add(Conv2D(32, kernel_size=(2, 2), input_shape=(128, 128, 3), padding = 'Same'))\n", - "model.add(Conv2D(32, kernel_size=(2, 2), activation ='relu', padding = 'Same'))\n", - "\n", - "\n", - "model.add(BatchNormalization())\n", - "model.add(MaxPooling2D(pool_size=(2, 2)))\n", - "model.add(Dropout(0.25))\n", - "\n", - "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n", - "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n", - "\n", - "model.add(BatchNormalization())\n", - "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n", - "model.add(Dropout(0.25))\n", - "\n", - "model.add(Flatten())\n", - "\n", - "model.add(Dense(512, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(2, activation='softmax'))\n", - "\n", - "model.compile(loss = \"categorical_crossentropy\", optimizer='Adam',metrics='accuracy')\n", - "print(model.summary())" - ], + "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -256,11 +215,10 @@ "id": "EYE5KlBz9ymO", "outputId": "ffb0b80e-d057-4311-8378-9da3a4a3e363" }, - "execution_count": 23, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Model: \"sequential_1\"\n", "_________________________________________________________________\n", @@ -306,13 +264,41 @@ "None\n" ] } + ], + "source": [ + "model = Sequential()\n", + "\n", + "model.add(Conv2D(32, kernel_size=(2, 2), input_shape=(128, 128, 3), padding = 'Same'))\n", + "model.add(Conv2D(32, kernel_size=(2, 2), activation ='relu', padding = 'Same'))\n", + "\n", + "\n", + "model.add(BatchNormalization())\n", + "model.add(MaxPooling2D(pool_size=(2, 2)))\n", + "model.add(Dropout(0.25))\n", + "\n", + "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n", + "model.add(Conv2D(64, kernel_size = (2,2), activation ='relu', padding = 'Same'))\n", + "\n", + "model.add(Conv2D(128, kernel_size=(2, 2), activation='relu', padding='Same'))\n", + "model.add(Conv2D(128, kernel_size=(2, 2), activation='relu', padding='Same'))\n", + "\n", + "model.add(BatchNormalization())\n", + "model.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n", + "model.add(Dropout(0.25))\n", + "\n", + "model.add(Flatten())\n", + "\n", + "model.add(Dense(512, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(2, activation='softmax'))\n", + "\n", + "model.compile(loss = \"categorical_crossentropy\", optimizer='Adam',metrics='accuracy')\n", + "print(model.summary())" ] }, { "cell_type": "code", - "source": [ - "history = model.fit(x_train, y_train, epochs = 30, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))" - ], + "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -320,11 +306,10 @@ "id": "cDguY7D_95-B", "outputId": "71fd42eb-ed18-4a10-b3d2-028b8d6d1740" }, - "execution_count": 24, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/30\n", "5/5 [==============================] - 13s 2s/step - loss: 19.0693 - accuracy: 0.7283 - val_loss: 63.3163 - val_accuracy: 0.5909\n", @@ -388,38 +373,23 @@ "5/5 [==============================] - 12s 2s/step - loss: 0.0771 - accuracy: 0.9827 - val_loss: 12.2145 - val_accuracy: 0.7500\n" ] } + ], + "source": [ + "history = model.fit(x_train, y_train, epochs = 30, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))" ] }, { "cell_type": "markdown", - "source": [ - "architecture-2" - ], "metadata": { "id": "1YBTELXNWPKt" - } + }, + "source": [ + "architecture-2" + ] }, { "cell_type": "code", - "source": [ - "model = Sequential()\n", - "model.add(Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=[128, 128, 3]))\n", - "model.add(MaxPooling2D(2, ))\n", - "model.add(Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu'))\n", - "model.add(MaxPooling2D(2))\n", - "\n", - "# Flatten the feature map\n", - "model.add(Flatten())\n", - "\n", - "# Add the fully connected layers\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.25))\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dense(2, activation='softmax'))\n", - "\n", - "# print the model summary\n", - "model.summary()" - ], + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -427,11 +397,10 @@ "id": "Yabzu23tAkrB", "outputId": "03cc3947-4005-451e-be14-2a9eac42b303" }, - "execution_count": 32, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Model: \"sequential_3\"\n", "_________________________________________________________________\n", @@ -464,14 +433,30 @@ "_________________________________________________________________\n" ] } + ], + "source": [ + "model = Sequential()\n", + "model.add(Conv2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu', input_shape=[128, 128, 3]))\n", + "model.add(MaxPooling2D(2, ))\n", + "model.add(Conv2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu'))\n", + "model.add(MaxPooling2D(2))\n", + "\n", + "# Flatten the feature map\n", + "model.add(Flatten())\n", + "\n", + "# Add the fully connected layers\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.25))\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dense(2, activation='softmax'))\n", + "\n", + "# print the model summary\n", + "model.summary()" ] }, { "cell_type": "code", - "source": [ - "model.compile(loss = \"categorical_crossentropy\", optimizer='Adam',metrics='accuracy')\n", - "history = model.fit(x_train, y_train, epochs = 10, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))" - ], + "execution_count": 34, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -479,11 +464,10 @@ "id": "TVtQ4cXMA2wW", "outputId": "33a9b081-b5b7-483b-cdb4-15a2fdc9afb8" }, - "execution_count": 34, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Epoch 1/10\n", "5/5 [==============================] - 5s 925ms/step - loss: 0.1670 - accuracy: 0.9595 - val_loss: 1.4972 - val_accuracy: 0.7500\n", @@ -507,33 +491,30 @@ "5/5 [==============================] - 4s 875ms/step - loss: 0.0157 - accuracy: 0.9942 - val_loss: 5.4253 - val_accuracy: 0.7273\n" ] } + ], + "source": [ + "model.compile(loss = \"categorical_crossentropy\", optimizer='Adam',metrics='accuracy')\n", + "history = model.fit(x_train, y_train, epochs = 10, batch_size = 40, verbose = 1,validation_data = (x_test, y_test))" ] }, { "cell_type": "code", + "execution_count": 54, + "metadata": { + "id": "S998yclcXnmH" + }, + "outputs": [], "source": [ "def names(number):\n", " if number==0:\n", " return 'Its a Tumor'\n", " else:\n", " return 'No, Its not a tumor'" - ], - "metadata": { - "id": "S998yclcXnmH" - }, - "execution_count": 54, - "outputs": [] + ] }, { "cell_type": "code", - "source": [ - "x = x_test[0]\n", - "x = x.reshape(1,128,128,3)\n", - "res = model.predict(x)\n", - "classification = np.where(res == np.amax(res))[1][0]\n", - "print(str(res[0][classification]*100) + '% Confidence This Is ' + names(classification))\n", - "cv2_imshow(x_test[0])" - ], + "execution_count": 56, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -542,37 +523,57 @@ "id": "hRXb2JpvWaSh", "outputId": "74d9234d-b0e2-4847-d8a5-64e28fb532ec" }, - "execution_count": 56, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "100.0% Confidence This Is Its a Tumor\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "x = x_test[0]\n", + "x = x.reshape(1,128,128,3)\n", + "res = model.predict(x)\n", + "classification = np.where(res == np.amax(res))[1][0]\n", + "print(str(res[0][classification]*100) + '% Confidence This Is ' + names(classification))\n", + "cv2_imshow(x_test[0])" ] }, { "cell_type": "code", - "source": [ - "" - ], + "execution_count": null, "metadata": { "id": "WiRTS8OBW8mg" }, - "execution_count": null, - "outputs": [] + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "name": "Brain_Tumor_detection.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +}