The goal of this project is to develop a deep learning model to classify kidney stones using the EfficientNet architecture.The final output of the project will be a trained deep learning model that can accurately classify kidney stones from images.
Data Used
The resulting dataset contains 12,446 unique data points, with cysts accounting for 3,709, normal findings for 5,077, stones for 1,377, and tumors for 2,283. To ensure ease of use, the data was split into train (80%), test (10%), and validation (10%) datasets. This dataset provides a comprehensive overview of kidney-related diagnoses, and can serve as an important resource for researchers and healthcare professionals alike.
2.2 Data Pre-processing and Data Augmentation
- Image Resizing: In order to ensure that our final model, Efficient-net 3, can accurately analyze the images we feed it, we need to resize all images to a common size of 224x250 pixels. This is necessary because the model is designed to work with images of a specific size and format. By standardizing all images to the same dimensions, we can ensure that the model can accurately compare and analyze them. This step is crucial for achieving accurate and reliable results from our image analysis. Therefore, we need to pay close attention to this step to ensure the quality of our final output.
- Image Normalization: Batch Normalizing was done to reduce the internal covariance shift, which is the phenomenon of the distribution of each layer's inputs changing during training. This is a problem because a neural network must learn the correct weights to produce an accurate output, and if the distribution of inputs is continually changing, the network won't be able to learn the correct weights.
- Image Augmentation: We take in a data frame, maximum samples, minimum samples, and a column as parameters. Then, it groups the data frame by the column specified and checks if the number of samples in any group is greater than the maximum number of samples specified. If so, it randomly samples the group to the maximum samples specified. It also checks if the number of samples in any group is less than the minimum number of samples specified.
- Image Filtering: Removing any unwanted noise from the images by applying filters such as median filter, Gaussian filter, sobel filter, etc., is an important step in ensuring that the data is accurate and reliable for further analysis. It also helps to ensure that all of the data is of uniform quality, that all of the samples are of the same size and shape, and that all the classes specified are present in the trimmed data frame.
- Feature Extraction: In order to extract features from the images, we need to first preprocess them. This involves resizing the images, normalizing the colors, and converting them to a suitable format for use in the model. Once the images have been preprocessed, we can extract important features such as shape, color, texture, etc.By leveraging these more complex features, we can further enhance the accuracy of the model.
- Data Preparation: This process of preparing the data set for training the model often requires a range of operations, including but not limited to, label encoding, one-hot encoding, feature scaling, normalization, and feature selection. Label encoding is the process of mapping a given set of categorical data labels to numerical values, and one-hot encoding is the process of creating a new feature for each unique value in a given categorical feature. Feature scaling, meanwhile, is the process of normalizing the range of values in a given feature, while normalization is the process of scaling the data to a specific range. Finally, feature selection is the process of selecting the most important features when training a model, which are often determined by the type of problem being solved.
Why Efficient-Net 3?
An empirical study has revealed that a balanced network width/depth/resolution can be achieved by scaling each of them with a constant ratio. In light of this observation, Efficient-Net B0 was created to be a highly beneficial yet straightforward compound scaling method for medical image datasets. This is an improvement over existing convolutional neural networks (CNNs) such as ResNet and Inception. The main concept of Efficient-Net is to leverage a combination of depthwise separable convolution, Squeeze-and-Excitation (SE) blocks, and a novel scaling algorithm to construct an efficient neural network architecture. The depthwise separable convolution is a form of convolution that divides a regular convolution into two distinct operations: a depthwise convolution and a pointwise convolution. The depthwise convolution is used to learn spatial features while the pointwise convolution is used to analyze channel-wise features. The Squeeze-and-Excitation block is an attention mechanism which amplifies important features and suppresses unimportant features to increase the network parameter efficiency. The novel scaling algorithm of Efficient-Net is based on the notion that the network should use fewer parameters as the input size increases. This scaling algorithm adjusts the network up or down depending on the input size, thus allowing for more efficient networks without sacrificing accuracy. All in all, Efficient-Net is an incredibly efficient neural network.
Steps To Inhance Image
Resolution Scaling
Width Scaling
Width scaling is an important component of EfficientNet, a family of state-of-the-art convolutional neural network architectures. It is a technique that automatically adjusts the width and depth of a model to maximize its accuracy. Width scaling works by increasing the number of channels for each convolutional layer in the network. This allows the model to learn more complex patterns, leading to increased accuracy. The idea behind width scaling is to adjust the model’s parameters in order to find the optimal combination of width and depth that maximizes its accuracy.
Depth Scaling
Depth Scaling of EfficientNet is a method used to improve the accuracy of a deep neural network. This method is based on the observation that deeper networks are more accurate than shallower ones. The idea behind this approach is to use a pre-trained model and then add additional layers to it. The number of layers added can be determined based on the desired accuracy. This approach allows for the network to become more accurate without having to increase the number of parameters.
