Hierarchical clustering technique for lung diseases

Abstract

Each year, pulmonary diseases are the underlying cause of death worldwide. The process of detecting lung diseases can be time-consuming and error-prone. Such errors can be expensive and aff ect patients’ lives. Accuracy and fast diagnosis are therefore crucial. Due to its high clinical impact and remaining challenges, medical image analysis has become a broad and active area of research in recent decades where various machine learning methods have been developed to assist in medical diagnosis. These machine learning models often use neural networks as a tool of image manip- ulation, feature extraction, and classifi cation and clustering techniques. Our proposed solution consists of using an alternative approach. In our study, distance and similarity measures have been applied to medical images hierarchical clustering in order to deter- mine their usability and accuracy in detecting lung diseases. Three indices were covered in this work in order to cluster chest X-rays: Euclidean distance, cosine distance, and Jensen-Shannon divergence. Each metric proposed has been applied to and tested on CheXphoto dataset with seven labels. Promising results were obtained with an accuracy range of 61.6% to 81.2% of correct predictions. Therefore, the proposed methods have a good application prospect and promotion value.