Image Processing Techniques for Acute Leukaemia Detection

Abstract

Every year a lot of people are diagnosed with blood cancer. This disease has a high mortality rate due to late and incorrect diagnoses. Acute leukaemia diagnosis requires an automated solution to facilitate early detection. This is one of the challenging problems and several machine learning techniques are proposed in recent years, however, image analysis using blood smear images remains the easiest and efficient technique to detect acute leukaemia. This study provides a literature review of the research work corresponding to the detection and classification of Acute lymphoblastic leukaemia (ALL) using digital image processing. The paper presents the analysis of various methods and techniques of image processing to detect leukaemia and highlights some of the research pros and cons. This literature review also introduces some of the research issues and challenges involved in this field of study