Pneumonia Detector for covid19

Labels:

CNN, Deep Learning, Convolutional Neural Networks, MobileNetV2, Transfer Learning, Tensorflow, Keras, Google Colabs,

Let's Start :)

The dataset I choosed is related the Covid-19 and the Pneumonia detection. It consists the x-ray images of various chests. It is more than 2GB and consists very different images. The link: kaggle.com/paultimothymooney/chest-xray-pneumonia I have made the dataset for tensorflow competible. My changes are seperation the data for binary classification while seperating test,train folders.The I have defined the train covid and normal(healthly) people folders.

datasetDir = os.path.join(my google drive path, 'dataset')
train_covid_dir = os.path.join(datasetDir, 'covid')
train_normal_dir = os.path.join(datasetDir, 'normal')

After that Keras function of “flow_from_directory” is used for creating train and the validation data as like tf.data.Dataset from image files in a directory. Then, for data augmentation; I’d like to see the data from the dataset. I have displayed the data and after investigation the all Images, realized that the number of normal and pneumonia image numbers. That is very informative for me while creating the model and configuring it. I need to avoid the overfitting problem so that I’d like to use data augmentation to create new images and enhance the model behavior. Data augmentation is used with thanks the function of ImageDataGenerator.This function takes some parameters like:

image_gen_train = ImageDataGenerator(
 rotation_range=15,
 width_shift_range=0.01,
 height_shift_range=0.01,
 rescale=1./255,
 shear_range=0.1,
 fill_mode='nearest',
 validation_split=0.2)

In my example, I created the images while rotating the input data and shifting width, height, and rescaling these. (for faster training time)

Although the ImageDataGenerator function does not simply do the additive operation as everyone wants, it allows us to train our model with the data of the magic 😊 function creates. We specify the images’ directories, and it generates the training and the validation augmented data for each epoch.

For example, if we have 100 images in total, with this function we will have another 100 different transformed and augmented or we can say enhanced images per epoch. After generating the new images for the data augmentation, we need to adapt these in train and test pools. I used following code for the purpose:

train_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
 directory=datasetDir,
shuffle=True,
 target_size=(IMG_HEIGHT, IMG_WIDTH),
 class_mode='binary')
val_data_gen = image_gen_train.flow_from_directory(batch_size=batch_size,
 directory=datasetDir,
 shuffle=True,
 target_size=(IMG_HEIGHT, IMG_WIDTH),
 class_mode='binary')

This flow_from_directory function is described in keras documentation as “generating function a tf.data.dataset from image files in a dir” and it must be called just after that the function of ImageDataGenerator. For question 2; I have downloaded the known model MobileNetV2 for my task. MobileNetV2 is a CNN architecture that is designed for the Image classification tasks.

I got the model like in the code:

base_model = tf.keras.applications.MobileNetV2(input_shape = (224, 224, 3),
include_top = False, weights = "imagenet")

The model named the base_model and the input shape as same as my x-ray images. Input is RGB and the weights of the downloaded model is get from the “imagenet” competition. After my model layer, structure defining I need to compile it as some parameters. I have used the Adam optimizer (the learning rate is decreased 0.00001 its because to gather smoother curve for better displaying and lower learning rate is better for this image learning due to my previous experiments) to and BinaryCrossentropy loss method is used also, metrics are described as their 'accuracy'.

base_learning_rate = 0.00001
model.compile(optimizer=tf.keras.optimizers.Adam(lr=base_learning_rate),
 loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
 metrics=['accuracy'])

And the most important step is generating the model. In this step model is learning itself from our dataset which we divided test and training. It is learning from its train folder and test itself as we expected from the folder name “test”. It is training from the “train_data_gen” folder, our predefined steps and epochs. Also same configs are in the validation.

history = model.fit_generator(
 train_data_gen,
 steps_per_epoch= train_data_gen.samples // batch_size,
 epochs=epochs,
 validation_data=val_data_gen,
 validation_steps= val_data_gen.samples // batch_size
)

In summary of part 2, transfer learning is a technique that is used many times in the literature. Thanks to transfer learning the pre-trained model for a different task can be used for different task second time. Comparison between unaugmented and the augmented data solutions of the MobileNetv2 model. Simply, In second time, I have called the image ImageDataGenerator functions without any parameter. It simply means that the func. Does not create any augmentation due to its handbook.

Old Accuracy results:

The idea and dataset comes from:

Adrian Rosebrock, PhD

Prof. Cem Unsalan, Marmara University

https://www.pyimagesearch.com/2020/03/16/detecting-covid-19-in-x-ray-images-with-keras-tensorflow-and-deep-learning/

https://github.com/ieee8023/covid-chestxray-dataset

https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia