RANDOMFOREST BASED CLASSIFICATION OF MEDICAL X-RAY IMAGES USING A GENETIC ALGORITHM FOR FEATURE SELECTION

Automated classification of medical images is an increasingly important tool for physicians in their daily activities. However, due to its computational complexity, this task is one of the major current challenges in the field of content-based image retrieval (CBIR). In this paper, a medical image classification approach is proposed. This method is composed of two main phases. The first step consists of a pre-processing, where a texture and shape based features vector is extracted. Also, a feature selection approach was applied by using a Genetic Algorithm (GA). The proposed GA uses a kNN based classification error as fitness function, which enables the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. In the second phase, a classification process is achieved by using random Forest classifier and a supervised multi-class classifier based on the support vector machine (SVM) for classifying X-ray images.