Blood cancer or leukemia detection using microscopic images is a challenging task considering the fact that variations in blood cell patterns are miniscule in nature and human detection may be prone to errors due to inherent deficiencies or anomalies in the dataset or due to human errors. Hence using automated classification has been considered using data pre-processing techniques such as Artificial Neural Networks and Fuzzy Logic. Recently, a new domain of research called neuro-fuzzy systems has garnered a lot of attention due to its efficacy. This paper introduces the challenges faced in the detection and classification of blood leukemia. Along with it, the paper focuses on the various significant contributions in the field by different researchers. This may pave the path for further improvement in accuracy of classification of leukemia.