An efficient classification and detection of abnormal ultrasound lung image based machine learning techniques

Abstract

Medical image processing had gained a lot of attention for medical diagnosis by disclosing internal structures of invisible regions in the human body. In recent history, lung disease is the most significant medical issues in medical world. Determining the maturity stage of the fetal lung is the challenging task at the earliest stage of pregnancy. The process of monitoring the fetus growth in the mother womb is a vital task during pregnancy. The immature growth of fetal lung leads to death during baby birth. Therefore, it is essential to diagnose the fetal lung maturity and to provide treatment at an early stage. The ultrasound imaging is the most commonly used technique for pregnancy scanning. It is performed due to high accuracy than any other scanning techniques. Computer Aided Diagnosis based image processing techniques are widely used in medical diagnosis for efficient classification. By implementing these CAD techniques the analysis of fetal lung maturity stage can be identified. The main aim of this research is to classify the fetal lung as mature and immature for early detection and to identify the abnormalities of the lung. newline