Image Data Predictor
Image-based prediction involves using machine learning or deep learning techniques to analyze and make predictions based on images or visual data. It has numerous applications in various fields, including medical imaging, autonomous vehicles, surveillance systems, and more. Convolutional Neural Networks (CNNs) are widely used for analyzing visual data due to their ability to capture spatial relationships and hierarchical features within images. We have developed a generalized Image-based prediction system powered by AI. In addition to performing prediction, our generalized system for image-based prediction also goes through several preprocessing procedures, such as scaling, cropping, or normalizing the images to guarantee consistency in size, format, and quality. It also offers several augmentation techniques like rotation, flipping, transpose, and many more for the variability and robustness of the model.The steps involved in our Image-based prediction are explained below.
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Quick Search and Input Dataset
Generic prediction for image data allows users to input any medical image dataset. Once the dataset is uploaded users can be viewed and deleted it.
Class Distribution and Sample Images
Users can view sample images along with the class distribution within the dataset.
Select Pre-Processing Operation
Users can perform different pre-processing operations such as resizing or augmentation of the images using the given Transformers.
Perform Training
Users can start training after selecting the desired model and other dependencies. Once the training is completed users can perform prediction.