diff --git a/CNN/cnn_tumor.py b/CNN/cnn_tumor.py new file mode 100644 index 0000000000000000000000000000000000000000..151d4f829d34a552d1ea6838c63500967d5acc0d --- /dev/null +++ b/CNN/cnn_tumor.py @@ -0,0 +1,120 @@ +import numpy as np +import matplotlib.pyplot as plt +import cv2 +import os +import tensorflow as tf +from PIL import Image +from sklearn.model_selection import train_test_split +from sklearn.metrics import classification_report +from tqdm import tqdm + + +image_dir=r"C:\Users\HP\Desktop\Devika_streamlit\CNN\tumordata" +no_tumor_images=os.listdir(r"C:\Users\HP\Desktop\Devika_streamlit\CNN\tumordata\no") +yes_tumor_images=os.listdir(r"C:\Users\HP\Desktop\Devika_streamlit\CNN\tumordata\yes") + + +dataset=[] +label=[] +img_siz=(128,128) + + +for i , image_name in tqdm(enumerate(no_tumor_images),desc="No Tumor"): + if(image_name.split('.')[1]=='jpg'): + image=cv2.imread(image_dir+'/no/'+image_name) + image=Image.fromarray(image,'RGB') + image=image.resize(img_siz) + dataset.append(np.array(image)) + label.append(0) + + +for i ,image_name in tqdm(enumerate(yes_tumor_images),desc="Tumor"): + if(image_name.split('.')[1]=='jpg'): + image=cv2.imread(image_dir+'/yes/'+image_name) + image=Image.fromarray(image,'RGB') + image=image.resize(img_siz) + dataset.append(np.array(image)) + label.append(1) + + +dataset=np.array(dataset) +label = np.array(label) + +print("--------------------------------------\n") +print('Dataset Length: ',len(dataset)) +print('Label Length: ',len(label)) +print("--------------------------------------\n") + + +print("--------------------------------------\n") +print("Train-Test Split") +x_train,x_test,y_train,y_test=train_test_split(dataset,label,test_size=0.2,random_state=42) +print("--------------------------------------\n") + +print("--------------------------------------\n") +print("Normalaising the Dataset. \n") + +# x_train=x_train.astype('float')/255 +# x_test=x_test.astype('float')/255 + +# Same step above is implemented using tensorflow functions. + +x_train=tf.keras.utils.normalize(x_train,axis=1) +x_test=tf.keras.utils.normalize(x_test,axis=1) + +print("--------------------------------------\n") + + +model=tf.keras.models.Sequential([ + tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(128,128,3)), + tf.keras.layers.MaxPooling2D((2,2)), + tf.keras.layers.Flatten(), + tf.keras.layers.Dense(256,activation='relu'), + tf.keras.layers.Dropout(0.5), + tf.keras.layers.Dense(512,activation='relu'), + tf.keras.layers.Dense(1,activation='sigmoid') +]) +print("--------------------------------------\n") +model.summary() +print("--------------------------------------\n") + +model.compile(optimizer='adam', + loss='binary_crossentropy', + metrics=['accuracy']) + + +print("--------------------------------------\n") +print("Training Started.\n") +history=model.fit(x_train,y_train,epochs=20,batch_size =128,validation_split=0.1) +print("Training Finished.\n") +print("--------------------------------------\n") + + + +print("--------------------------------------\n") + +print("Model Evalutaion Phase.\n") +loss,accuracy=model.evaluate(x_test,y_test) +print(f'Accuracy: {round(accuracy*100,2)}') +print("--------------------------------------\n") + + +y_pred=model.predict(x_test) +y_pred = (y_pred > 0.5).astype(int) +print('classification Report\n',classification_report(y_test,y_pred)) +print("--------------------------------------\n") + + +def make_prediction(img,model): + img=cv2.imread(img) + img=Image.fromarray(img) + img=img.resize((128,128)) + img=np.array(img) + input_img = np.expand_dims(img, axis=0) + res = model.predict(input_img) + if res: + print("Tumor Detected") + else: + print("No Tumor") + +model.save(r'C:\Users\HP\Desktop\Devika_streamlit\CNN_model.h5') \ No newline at end of file diff --git a/CNN/tumordata/Whole Dataset/TEST/annotations_test.json b/CNN/tumordata/Whole Dataset/TEST/annotations_test.json new file mode 100644 index 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