Deep-learning in Health care || Image Classification using(VGG16)?
Science & Technology
Introduction
In today's session, we will explore how to build an image classification model using deep learning techniques, specifically Convolutional Neural Networks (CNNs). The focus will be on a brain MRI dataset and utilizing a pre-trained model called VGG16. For those who may not be familiar with deep learning or CNNs, it is often recommended for beginners to leverage pre-trained models as it simplifies the process significantly.
Dataset Overview
For this tutorial, we will use a brain MRI image dataset available on Kaggle, containing two classifications: 'yes' (indicating the presence of a brain tumor) and 'no' (indicating a normal MRI). The dataset comprises 253 images: approximately 99 images in the 'no' class and 192 in the 'yes' class.
Importing the Data
To work with the dataset in Google Colab, you must first connect to your Kaggle account and retrieve your API key. This API key allows you to download the dataset directly into your working environment. After downloading, the dataset, which will be in ZIP format, needs to be extracted to access the image files.
Data Preparation
Once the images are extracted, we organize them into two folders corresponding to their classes ('yes' and 'no'). Subsequently, we will create two arrays: one to hold the image data and another to hold their associated labels. Using Python libraries like NumPy and TQDM (for progress bar visualization), we can check the copying progress of images from the original folders into one consolidated array.
We must divide the dataset into a training set and a testing set, using a ratio of 77% for training and 33% for testing. To facilitate model training, we convert the labels from strings to integers using label encoding.
Building the Model with VGG16
Instead of constructing a CNN from scratch, we will utilize the pre-trained VGG16 model. This model has been trained on the ImageNet dataset, which contains approximately 20,000 classes. Since our task involves only two classes, we need to modify the top layer of the VGG16 model.
To implement this modification, we load the VGG16 model without its top layers, making the pre-trained layers non-trainable while adding new layers specific to our classification task. The new layers we will include consist of a Global Average Pooling layer, a couple of Dense layers, and an output layer specifying 2 classes, with a softmax activation function.
After compiling the model with an Adam optimizer and categorical cross-entropy loss function, we proceed to fit the model to our training data. Upon successful training, we should expect to see performance metrics, with accuracy potentially reaching around 91.72%.
Conclusion
Through the use of a pre-trained model like VGG16, we have effectively built an image classification model with relative ease. This approach demonstrates the power and efficiency of transfer learning in healthcare applications, specifically in processing medical images.
Keywords
Image Classification, Deep Learning, Convolutional Neural Networks, VGG16, Brain MRI, Transfer Learning, Pre-trained Models, Kaggle, Data Preparation, Label Encoding.
FAQ
1. What is transfer learning in deep learning?
Transfer learning refers to the practice of leveraging a pre-trained model on a new problem, allowing for quicker training and improved performance.
2. Why should I use pre-trained models like VGG16?
Pre-trained models have already learned useful features from vast datasets, which saves significant time and computational resources compared to training a model from scratch.
3. What type of data can I classify using image classification techniques?
Image classification techniques can be applied to various types of visual data, including medical imaging (like MRIs), everyday objects, animals, and more.
4. What is the importance of data preparation in building a model?
Proper data preparation, including cleaning, organizing, and encoding data, is crucial as it directly affects the model's performance and training efficiency.
5. How can I improve my model's accuracy?
You can improve model accuracy through techniques such as increasing the dataset size, fine-tuning hyperparameters, or employing advanced data augmentation methods.