**3.7 The system flowchart**

Image Pre-processing: The aim of this process is to improve the image data (features) by suppressing unwanted distortions and enhancement of some important image features so that our Computer Vision models can benefit from this improved

**Figure 6.** *Flowchart diagram of the classification and prediction.*

*Ensemble Machine Learning Algorithms for Prediction and Classification of Medical Images DOI: http://dx.doi.org/10.5772/intechopen.100602*

data to work on. Detection of an object: Detection refers to the localization of an object which means the segmentation of the image and identifying the position of the object of interest. Feature extraction and Training: This is a crucial step wherein statistical or deep learning methods are used to identify the most interesting patterns of the image, features that might be unique to a particular class, and that will, later on, help the model to differentiate between different classes. This process where the model learns the features from the dataset is called model training. Classification of the object: This step categorizes detected objects into predefined classes by using a suitable classification technique that compares the image patterns with the target patterns (**Figure 6**).
