Acute lymphoid leukemia classification using two-step neural network classifier

Leukemia induced death has been listed in the top ten most dangerous mortality cause for human being. Among many reasons, one of them is the slow decision-making process which make suitable medical treatment cannot be applied on time. Therefore, good clinical decision support system for acute leukemia type classification has always become necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using sequential neural network classifier. Our experimental result only cover the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further research is needed to proof the second neural network classifier effectiveness performance.