Chest Disease Classification Using Convolutional Neural Network Algorithm
A. PREPROCESSING
import numpy as np
import matplotlib.pyplot as plt
import os
import cv2
from tqdm import tqdm
DATADIR = "D:\\"
CATEGORIES = ["NORMAL","PNEUMONIA"]
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
for img in os.listdir(path):
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
plt.imshow(img_array, cmap='gray')
plt.show()
break
break
print(img_array.shape)
IMG_SIZE = 100
new_array = cv2.resize(img_array,(IMG_SIZE,IMG_SIZE))
plt.imshow(new_array, cmap='gray')
plt.show()
training_data = []
def create_training_data():
for category in CATEGORIES:
path = os.path.join(DATADIR, category)
print(path)
class_num = CATEGORIES.index(category)
print(class_num)
for img in tqdm(os.listdir(path)):
try:
img_array = cv2.imread(os.path.join(path,img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
training_data.append([new_array, class_num])
except Exception as e:
pass
create_training_data()
print(len(training_data))
X = []
y = []
for features,label in training_data:
X.append(features)
y.append(label)
print(X[0].reshape(-1, IMG_SIZE, IMG_SIZE, 1))
X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)
import pickle
pickle_out = open("X.pickle","wb")
pickle.dump(X, pickle_out)
pickle_out.close()
pickle_out = open("y.pickle","wb")
pickle.dump(y, pickle_out)
pickle_out.close()
pickle_in = open("x.pickle","rb")
pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
pickle.load(pickle_in)
B. MODELING CNN
# -*- coding: utf-8 -*-
"""
Created on Wed Apr 1 15:14:14 2020
@author: Renal
"""
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten
from tensorflow.keras.layers import Conv2D, MaxPooling2D
# more info on callbakcs: https://keras.io/callbacks/ model saver is cool too.
#from tensorflow.keras.callbacks import TensorBoard
import pickle
import time
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X = X/255.0 # Normalisasi
dense_layers = [0]
layer_sizes = [64]
conv_layers = [3]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer, layer_size, dense_layer, int(time.time()))
print(NAME)
model = Sequential()
model.add(Conv2D(layer_size, (3, 3), input_shape=X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
for l in range(conv_layer-1):
model.add(Conv2D(layer_size, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
for _ in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation('relu'))
model.add(Dense(1))
model.add(Activation('sigmoid'))
#tensorboard = TensorBoard(log_dir="C:\\oke\\{}".format(NAME))
model.compile(loss='binary_crossentropy',
optimizer='adam',
metrics=['accuracy'],
)
model.fit(X, y,
batch_size=32,
epochs=10,
validation_split=0.2)
model.save('Tugas-Comvis.model')
print("model telah disimpan")
C. PREDICT
Created on Wed Apr 1 15:21:24 2020
@author: Renal
"""
import cv2
import tensorflow as tf
CATEGORIES = ["NORMAL", "PNEUMONIA"]
def prepare(filepath):
IMG_SIZE = 100 # 50 in txt-based
img_array = cv2.imread(filepath, cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
return new_array.reshape(-1, IMG_SIZE, IMG_SIZE, 1)
model = tf.keras.models.load_model("Tugas-Comvis.model")
prediction = model.predict([prepare('person5_bacteria_19.jpeg')])
print('Kategori = ',int(prediction[0][0])) # will be a list in a list.
print('Klasifikasi Paru-Paru = ',CATEGORIES[int(prediction[0][0])])
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