Added the Lung cancer detection using CNN

MachineLearning Projects/Lung Cancer Detection using CNN/Lung Cancer Detection Using CNN

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+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+import seaborn as sns
+import keras
+from keras.models import Sequential
+from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout , BatchNormalization
+from keras.preprocessing.image import ImageDataGenerator
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import classification_report,confusion_matrix
+from keras.callbacks import ReduceLROnPlateau
+import cv2
+import os
+
+labels = ['PNEUMONIA', 'NORMAL']
+img_size = 150
+def get_training_data(data_dir):
+    data = [] 
+    for label in labels: 
+        path = os.path.join(data_dir, label)
+        class_num = labels.index(label)
+        for img in os.listdir(path):
+            try:
+                img_arr = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
+                resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size
+                data.append([resized_arr, class_num])
+            except Exception as e:
+                print(e)
+    return np.array(data)
+
+train = get_training_data('/content/drive/MyDrive/chest-xray-pneumonia/chest_xray/train')
+test = get_training_data('/content/drive/MyDrive/chest-xray-pneumonia/chest_xray/test')
+val = get_training_data('/content/drive/MyDrive/chest-xray-pneumonia/chest_xray/val')
+
+l = []
+for i in train:
+    if(i[1] == 0):
+        l.append("Pneumonia")
+    else:
+        l.append("Normal")
+sns.set_style('darkgrid')
+sns.countplot(l)
+
+plt.figure(figsize = (5,5))
+plt.imshow(train[0][0], cmap='gray')
+plt.title(labels[train[0][1]])
+
+plt.figure(figsize = (5,5))
+plt.imshow(train[-1][0], cmap='gray')
+plt.title(labels[train[-1][1]])
+
+x_train = []
+y_train = []
+
+x_val = []
+y_val = []
+
+x_test = []
+y_test = []
+
+for feature, label in train:
+    x_train.append(feature)
+    y_train.append(label)
+
+for feature, label in test:
+    x_test.append(feature)
+    y_test.append(label)
+
+for feature, label in val:
+    x_val.append(feature)
+    y_val.append(label)
+
+x_train = np.array(x_train) / 255
+x_val = np.array(x_val) / 255
+x_test = np.array(x_test) / 255
+
+x_train = x_train.reshape(-1, img_size, img_size, 1)
+y_train = np.array(y_train)
+
+x_val = x_val.reshape(-1, img_size, img_size, 1)
+y_val = np.array(y_val)
+
+x_test = x_test.reshape(-1, img_size, img_size, 1)
+y_test = np.array(y_test)
+
+datagen = ImageDataGenerator(
+        featurewise_center=False,
+        samplewise_center=False,
+        featurewise_std_normalization=False,
+        samplewise_std_normalization=False,
+        zca_whitening=False,
+        rotation_range = 30,
+        zoom_range = 0.2,
+        width_shift_range=0.1,
+        height_shift_range=0.1,
+        horizontal_flip = True,
+        vertical_flip=False)
+
+
+datagen.fit(x_train)
+
+model = Sequential()
+model.add(Conv2D(32 , (3,3) , strides = 1 , padding = 'same' , activation = 'relu' , input_shape = (150,150,1)))
+model.add(BatchNormalization())
+model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
+model.add(Conv2D(64 , (3,3) , strides = 1 , padding = 'same' , activation = 'relu'))
+model.add(Dropout(0.1))
+model.add(BatchNormalization())
+model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
+model.add(Conv2D(64 , (3,3) , strides = 1 , padding = 'same' , activation = 'relu'))
+model.add(BatchNormalization())
+model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
+model.add(Conv2D(128 , (3,3) , strides = 1 , padding = 'same' , activation = 'relu'))
+model.add(Dropout(0.2))
+model.add(BatchNormalization())
+model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
+model.add(Conv2D(256 , (3,3) , strides = 1 , padding = 'same' , activation = 'relu'))
+model.add(Dropout(0.2))
+model.add(BatchNormalization())
+model.add(MaxPool2D((2,2) , strides = 2 , padding = 'same'))
+model.add(Flatten())
+model.add(Dense(units = 128 , activation = 'relu'))
+model.add(Dropout(0.2))
+model.add(Dense(units = 1 , activation = 'sigmoid'))
+model.compile(optimizer = "rmsprop" , loss = 'binary_crossentropy' , metrics = ['accuracy'])
+model.summary()
+
+learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', patience = 2, verbose=1,factor=0.3, min_lr=0.000001)
+
+history = model.fit(datagen.flow(x_train,y_train, batch_size = 32) ,epochs = 12 , validation_data = datagen.flow(x_val, y_val) ,callbacks = [learning_rate_reduction])
+
+print("Loss of the model is - " , model.evaluate(x_test,y_test)[0])
+print("Accuracy of the model is - " , model.evaluate(x_test,y_test)[1]*100 , "%")
+
+epochs = [i for i in range(12)]
+fig , ax = plt.subplots(1,2)
+train_acc = history.history['accuracy']
+train_loss = history.history['loss']
+val_acc = history.history['val_accuracy']
+val_loss = history.history['val_loss']
+fig.set_size_inches(20,10)
+
+ax[0].plot(epochs , train_acc , 'go-' , label = 'Training Accuracy')
+ax[0].plot(epochs , val_acc , 'ro-' , label = 'Validation Accuracy')
+ax[0].set_title('Training & Validation Accuracy')
+ax[0].legend()
+ax[0].set_xlabel("Epochs")
+ax[0].set_ylabel("Accuracy")
+
+ax[1].plot(epochs , train_loss , 'g-o' , label = 'Training Loss')
+ax[1].plot(epochs , val_loss , 'r-o' , label = 'Validation Loss')
+ax[1].set_title('Testing Accuracy & Loss')
+ax[1].legend()
+ax[1].set_xlabel("Epochs")
+ax[1].set_ylabel("Training & Validation Loss")
+plt.show()
+
+predictions = model.predict(x_test)
+predictions = predictions.reshape(1,-1)[0]
+y_pred = np.where(predictions > 0.5,1,0)
+predictions[:15]
+
+print(classification_report(y_test, y_pred, target_names = ['Pneumonia (Class 0)','Normal (Class 1)']))
+
+cm = confusion_matrix(y_test,y_pred)
+cm
+
+cm = pd.DataFrame(cm , index = ['0','1'] , columns = ['0','1'])
+
+correct = np.nonzero(predictions == y_test)[0]
+incorrect = np.nonzero(predictions != y_test)[0]
+
+i = 0
+for c in correct[:6]:
+    plt.subplot(3,2,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.imshow(x_test[c].reshape(150,150), cmap="gray", interpolation='none')
+    plt.title("Predicted Class {},Actual Class {}".format(predictions[c], y_test[c]))
+    plt.tight_layout()
+    i += 1
+
+i = 0
+for c in incorrect[:6]:
+    plt.subplot(3,2,i+1)
+    plt.xticks([])
+    plt.yticks([])
+    plt.imshow(x_test[c].reshape(150,150), cmap="gray", interpolation='none')
+    plt.title("Predicted Class {},Actual Class {}".format(predictions[c], y_test[c]))
+    plt.tight_layout()
+    i += 1

MachineLearning Projects/Lung Cancer Detection using CNN/readme.md

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+# Detecting face from photo or camera
+___
+### Setup
+`pip install -r requirements.txt`
+
+___
+### Usage
+`python3 main.py` Detection of lung cancer using CNN
+
+___
+Here the data is taken from the kaggle.com
+
+Dataset Link:https://www.kaggle.com/code/tarunm561/lung-cancer-detection-using-cnn/input

MachineLearning Projects/Lung Cancer Detection using CNN/requirements.txt

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+pandas
+sea-born
+keras
+matplotlib