By Kidney Stone Detection Using by Python
What Do We Want?
The objectives and desired outcomes of this project on kidney stone detection using Convolutional Neural Networks (CNNs) and image segmentation techniques
Methodology:
In this section, we will outline the methodology used for the kidney stone detection project. We will discuss the steps involved in collecting and preprocessing the dataset from Kaggle.com. We will also explain the implementation of the Convolutional Neural Network (CNN) using Python language, TensorFlow, and Keras. Additionally, we will provide details on the training and evaluation process of the model.
First Step
First, you need to install Jupyter Notebook for writing code and visualizing graph patterns or simply create an account on Google Colab and write code here: https://colab.google/. In this project, we use Google Colab, the Python programming language, and Python’s built-in libraries. Python: Python is a popular programming language widely used in the field of machine learning and computer vision. It provides a rich ecosystem of libraries and frameworks, such as TensorFlow, PyTorch, and Keras, which can be used to develop and train CNN models for kidney stone detection. Google Colab: Google Colab provides an interactive and collaborative environment for developing and documenting machine learning projects. It allows for code execution, visualizations, and explanations to be combined in a single document, making it easier to share and reproduce the project.
Deep Learning Frameworks: TensorFlow and PyTorch are powerful deep learning frameworks that provide a high-level interface for building and training neural networks. These frameworks offer various pre-built models, optimization algorithms, and evaluation metrics, facilitating the development and evaluation of the kidney stone detection system.
Image Processing Libraries: Libraries like OpenCV (Open Source Computer Vision Library) can be used for preprocessing and manipulating CT scan images. OpenCV provides a wide range of image processing functions, including filtering, segmentation, and feature extraction, which can aid in the preprocessing steps of the kidney stone detection pipeline.
Visualization Libraries: Libraries like Matplotlib, Seaborn, or Plotly can be used to create visualizations of evaluation metrics, segmentation results, or other relevant data. These libraries provide flexible and customizable options for creating informative and visually appealing plots and charts.
Second Step
Data Collection: The next step involves collecting annotated datasets of CT scan images that include kidney stones. These datasets will serve as the basis for training and evaluating the CNN models. The data collection process will include obtaining permissions, adhering to ethical guidelines, and ensuring the datasets represent a diverse range of kidney stone cases.
6.2.1. Identify Data Sources: The first step in data collection is to identify potential sources of CT scan images that include kidney stones. These sources may include hospitals, medical research institutions, public databases, or collaborations with healthcare professionals. It is important to ensure that the data sources comply with ethical guidelines and patient privacy regulations.
Including Kidney Stone Dataset Like:
Excluding Kidney Stone Dataset Like:
Obtain Permissions: Once potential data sources are identified, it is necessary to obtain the required permissions and approvals to access and use the data.
Annotation and Ground Truth: To train and evaluate the CNN models and image segmentation techniques, it is crucial to have annotated datasets where the presence and location of kidney stones are marked. This annotation process can be done manually by radiologists or trained professionals. The annotations serve as ground truth for training and evaluation purposes.
Dataset Diversity: It is important to ensure that the collected dataset represents a diverse range of kidney stone cases. This includes considering factors such as stone size, shape, location, composition, and patient demographics. A diverse dataset will help in training the models to generalize well and accurately detect kidney stones in various scenarios.
Data Preprocessing: Once the CT scan images and corresponding annotations are obtained, preprocessing may be required. This may involve resizing the images to a standardized resolution, normalizing the intensity values, removing noise or artifacts, and other preprocessing techniques to enhance the quality and consistency of the input data.
Data Augmentation: To further enhance the robustness and generalization of the trained models, data augmentation techniques can be applied. This involves generating additional training samples by applying transformations such as rotation, scaling, flipping, or adding noise to the original data.
Data Split: The collected dataset needs to be divided into appropriate subsets for training, validation, and testing purposes. A common approach is to split the data into 70-80% for training, 10-15% for validation, and 10-15% for testing. This ensures that the models are trained on a sufficient amount of data, validated on a separate set, and tested on unseen data to assess their performance.
Ethical Considerations: Throughout the data collection process, it is crucial to adhere to ethical guidelines and patient privacy regulations. This includes obtaining proper consent, ensuring data anonymization and de-identification, and maintaining the confidentiality and security of the collected data.
Preprocessing:
The collected CT scan images will undergo preprocessing to enhance their quality and standardize them for analysis. This may involve resizing, normalization, noise reduction
. Image Resizing: CT scan images may have varying resolutions, so resizing them to a standardized resolution is important to ensure consistency during training and evaluation. Resizing can be done to a specific pixel size or aspect ratio suitable for the CNN model being used.
Intensity Normalization: CT scan images often have varying intensity ranges due to differences in acquisition parameters or scanner settings. Normalizing the intensity values helps to standardize the input data and improve the performance of the CNN model. Common normalization techniques include linear scaling, histogram equalization, or z-score normalization.
Noise Reduction: CT scan images may contain noise or artifacts that can affect the accuracy of kidney stone detection. Applying noise reduction techniques, such as Gaussian filtering or median filtering, can help to reduce noise and enhance the clarity of the images.
Contrast Enhancement: Enhancing the contrast of the CT scan images can improve the visibility of kidney stones and make them more distinguishable from the surrounding tissues. Techniques such as contrast stretching, histogram equalization, or adaptive histogram equalization can be applied to enhance the contrast.
Region of Interest (ROI) Extraction: To focus the analysis on the relevant regions containing the kidneys and potential stones, a region of interest (ROI) can be extracted. This can be done by segmenting the kidneys using image segmentation techniques or by manually selecting the ROI based on anatomical landmarks.
Data Augmentation: As mentioned earlier, data augmentation techniques can be applied during preprocessing to increase the diversity and robustness of the training data. This can involve randomly applying transformations such as rotation, scaling, flipping, or adding noise to generate additional training samples.
Data Standardization: It is important to standardize the data by normalizing the pixel values to a common scale or range. This ensures that the CNN model receives consistent input data, regardless of the original intensity values or image resolutions.
Data Format Conversion: The CT scan images may be in different file formats, such as DICOM or NIfTI. Converting the images to a suitable format, such as JPEG or PNG, can facilitate data handling and compatibility with the CNN model.
Quality Control: Throughout the preprocessing steps, it is essential to perform quality control checks to ensure that the images are properly processed without any loss of critical information or introduction of artifacts. Visual inspection and validation against ground truth annotations can help identify any issues or discrepancies.
By applying these preprocessing steps, the CT scan images can be enhanced and standardized, improving the accuracy and reliability of kidney stone detection using CNNs and image segmentation techniques.
Training and Validation:
The selected CNN model(s) will be trained on the annotated datasets using a suitable training algorithm, such as stochastic gradient descent or Adam optimization. The training process will involve feeding the CT scan images as input to the model, adjusting the model’s weights through backpropagation, and iteratively optimizing the model’s performance. The trained model(s) will be validated using separate validation datasets to assess their accuracy and generalization capabilities
Available Data: Assess the size and characteristics of the available data. Determine the number of CT scan images with kidney stones and the distribution of different stone types, sizes, and locations. Ensure that the dataset is representative of the population you aim to detect kidney stones in.
Annotated Data: Identify the subset of the available data that has been annotated with ground truth labels indicating the presence and location of kidney stones. This annotated data will be used for training and evaluating the CNN model.
Training Set: Select a portion of the annotated data for training the CNN model. The training set should be large enough to capture the underlying patterns and variations in kidney stones, while also allowing the model to generalize well to unseen data. As a guideline, allocate around 70-80% of the annotated data for training.
import matplotlib.pyplot as plt
plt.plot(history.history[‘accuracy’],color=’red’,label=’train’)
plt.plot(history.history[‘val_accuracy’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
plt.plot(history.history[‘accuracy’],color=’red’,label=’train’)
plt.plot(history.history[‘val_accuracy’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
Validation Set: Reserve a separate portion of the annotated data as a validation set. This set will be used to evaluate and fine-tune the model during the training process. Allocate around 10-15% of the annotated data for validation.
plt.plot(history.history[‘loss’],color=’red’,label=’train’)
plt.plot(history.history[‘val_loss’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
plt.plot(history.history[‘loss’],color=’red’,label=’train’)
plt.plot(history.history[‘val_loss’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
Stratified Sampling: Consider stratified sampling to ensure that the distribution of kidney stones within the training and validation sets reflects the overall distribution in the dataset. This helps to prevent bias and ensures that the model is trained on a representative range of cases.
Randomization: Randomly shuffle the annotated data before splitting it into training and validation sets. This helps to ensure that the data is evenly distributed across the sets and reduces the risk of any systematic biases or patterns.
Data Balance: Check for class imbalance in the annotated data, i.e., if there are significantly more instances of one class (with kidney stones) compared to the other class (without kidney stones). If there is a severe imbalance, consider techniques such as oversampling, undersampling, or class weighting to address the issue and prevent the model from being biased towards the majority class.
Cross-Validation: Consider using cross-validation techniques, such as k-fold cross-validation, if the dataset size is limited. This involves splitting the annotated data into multiple folds and training the model on different combinations of training and validation sets. This helps to obtain more reliable performance estimates and assess the model’s generalization capabilities.
Data Augmentation: During the training process, apply data augmentation techniques to artificially increase the diversity of the training set. This can include random rotations, translations, scaling, and flipping of the CT scan images.
Code:
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!kaggle datasets download -d nazmul0087/ct-kidney-dataset-normal-cyst-tumor-and-stone
Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run ‘chmod 600 /root/.kaggle/kaggle.json’
Downloading ct-kidney-dataset-normal-cyst-tumor-and-stone.zip to /content
99% 1.51G/1.52G [00:12<00:00, 287MB/s]
100% 1.52G/1.52G [00:12<00:00, 135MB/s]
[ ]
import zipfile
zip_ref=zipfile.ZipFile(‘/content/ct-kidney-dataset-normal-cyst-tumor-and-stone.zip‘,’r’)
zip_ref.extractall(‘/content’)
zip_ref.close()
import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,BatchNormalization,Dropout
# generators
train_ds = keras.utils.image_dataset_from_directory(
directory = ‘/content/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone’,
labels=’inferred’,
label_mode = ‘int’,
batch_size=32,
image_size=(256,256)
)
validation_ds = keras.utils.image_dataset_from_directory(
directory = ‘/content/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone/CT-KIDNEY-DATASET-Normal-Cyst-Tumor-Stone’,
labels=’inferred’,
label_mode = ‘int’,
batch_size=32,
image_size=(256,256)
)
Found 12446 files belonging to 4 classes.
Found 12446 files belonging to 4 classes.
# Normalize
def process(image,label):
image = tf.cast(image/255. ,tf.float32)
return image,label
train_ds = train_ds.map(process)
validation_ds = validation_ds.map(process)
create cnn model
model = Sequential()
model.add(Conv2D(32,kernel_size=(3,3),padding=’valid’,activation=’relu’,input_shape=(256,256,3)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding=’valid’))
model.add(Conv2D(64,kernel_size=(3,3),padding=’valid’,activation=’relu’))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding=’valid’))
model.add(Conv2D(128,kernel_size=(3,3),padding=’valid’,activation=’relu’))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2),strides=2,padding=’valid’))
model.add(Flatten())
model.add(Dense(128,activation=’relu’))
model.add(Dropout(0.1))
model.add(Dense(64,activation=’relu’))
model.add(Dropout(0.1))
model.add(Dense(1,activation=’sigmoid’))
model.summary()
Model: “sequential”
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 254, 254, 32) 896
batch_normalization (BatchN (None, 254, 254, 32) 128
ormalization)
max_pooling2d (MaxPooling2D (None, 127, 127, 32) 0
)
conv2d_1 (Conv2D) (None, 125, 125, 64) 18496
batch_normalization_1 (Batc (None, 125, 125, 64) 256
hNormalization)
max_pooling2d_1 (MaxPooling (None, 62, 62, 64) 0
2D)
conv2d_2 (Conv2D) (None, 60, 60, 128) 73856
batch_normalization_2 (Batc (None, 60, 60, 128) 512
hNormalization)
max_pooling2d_2 (MaxPooling (None, 30, 30, 128) 0
2D)
flatten (Flatten) (None, 115200) 0
dense (Dense) (None, 128) 14745728
dropout (Dropout) (None, 128) 0
dense_1 (Dense) (None, 64) 8256
dropout_1 (Dropout) (None, 64) 0
dense_2 (Dense) (None, 1) 65
=================================================================
Total params: 14,848,193
Trainable params: 14,847,745
Non-trainable params: 448
_________________________________________________________________
[ ]
model.compile(optimizer=’adam’,loss=’binary_crossentropy’,metrics=[‘accuracy’])
[ ]
history = model.fit(train_ds,epochs=10,validation_data=validation_ds)
Epoch 1/10
389/389 [==============================] – 108s 236ms/step – loss: -8681859.0000 – accuracy: 0.4302 – val_loss: -16510143.0000 – val_accuracy: 0.4079
Epoch 2/10
389/389 [==============================] – 91s 233ms/step – loss: -250113536.0000 – accuracy: 0.4438 – val_loss: -445791232.0000 – val_accuracy: 0.4079
Epoch 3/10
389/389 [==============================] – 94s 239ms/step – loss: -1727355520.0000 – accuracy: 0.4507 – val_loss: -2375478016.0000 – val_accuracy: 0.4079
Epoch 4/10
389/389 [==============================] – 89s 227ms/step – loss: -6524577280.0000 – accuracy: 0.4562 – val_loss: -7082071040.0000 – val_accuracy: 0.4195
Epoch 5/10
389/389 [==============================] – 91s 232ms/step – loss: -17804419072.0000 – accuracy: 0.4614 – val_loss: -25752240128.0000 – val_accuracy: 0.4630
Epoch 6/10
389/389 [==============================] – 93s 238ms/step – loss: -38137327616.0000 – accuracy: 0.4604 – val_loss: -59395207168.0000 – val_accuracy: 0.4079
Epoch 7/10
389/389 [==============================] – 91s 231ms/step – loss: -70426853376.0000 – accuracy: 0.4556 – val_loss: -105070190592.0000 – val_accuracy: 0.4079
Epoch 8/10
389/389 [==============================] – 91s 232ms/step – loss: -117277974528.0000 – accuracy: 0.4573 – val_loss: -142653325312.0000 – val_accuracy: 0.4600
Epoch 9/10
389/389 [==============================] – 93s 238ms/step – loss: -182575120384.0000 – accuracy: 0.4538 – val_loss: -197918916608.0000 – val_accuracy: 0.4327
Epoch 10/10
389/389 [==============================] – 134s 342ms/step – loss: -267304779776.0000 – accuracy: 0.4544 – val_loss: -283651866624.0000 – val_accuracy: 0.5832
[ ]
import matplotlib.pyplot as plt
plt.plot(history.history[‘accuracy’],color=’red’,label=’train’)
plt.plot(history.history[‘val_accuracy’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
import matplotlib.pyplot as plt
plt.plot(history.history[‘accuracy’],color=’red’,label=’train’)
plt.plot(history.history[‘val_accuracy’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
plt.plot(history.history[‘accuracy’],color=’red’,label=’train’)
plt.plot(history.history[‘val_accuracy’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
plt.plot(history.history[‘loss’],color=’red’,label=’train’)
plt.plot(history.history[‘val_loss’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
plt.plot(history.history[‘loss’],color=’red’,label=’train’)
plt.plot(history.history[‘val_loss’],color=’blue’,label=’validation’)
plt.legend()
plt.show()
# ways to reduce overfitting
# Add more data
# Data Augmentation -> next video
# L1/L2 Regularizer
# Dropout
# Batch Norm
# Reduce complexity
import cv2
test_img = cv2.imread(‘/content/CTkidney.jpg’)
plt.imshow(test_img)
<matplotlib.image.AxesImage at 0x7d22e05417e0>
test_img.shape
(1063, 1200, 3)
test_img = cv2.resize(test_img,(256,256))
test_input = test_img.reshape((1,256,256,3))
model.predict(test_input)
1/1 [==============================] – 0s 329ms/step
array([[1.]], dtype=float32)
test_img = cv2.imread(‘/content/faulty kidny.jpeg’)
plt.imshow(test_img)
<matplotlib.image.AxesImage at 0x7d22e028d390>
test_img.shape
(234, 215, 3)
test_img = cv2.resize(test_img,(256,256))
test_input = test_img.reshape((1,256,256,3))
model.predict(test_input)
1/1 [==============================] – 0s 39ms/step
array([[0.]], dtype=float32)
Result:
When input a kidney image which collect from CT Scan Then the Output Array Will be Show [ ‘0’ 0r ‘1’ ]
Including stone the output will be “0” of kidney image and excluding stone the output array show “1”
Input a normal kidney image:-
Output a normal kidney image:-
Input an Including Stone kidney image:-
Output an Including Stone kidney image:-
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