Diagnosis of Leukemia Disease Based on Enhanced Virtual Neural Network

White Blood Cell (WBC) cancer or leukemia is one of the serious cancers that threaten existence human beings. In spite its prevalence and consequences, it mostly diagnosed through manual practices. The risks inappropriate, sub-standard wrong biased diagnosis are high in methods. So, there a need exists for automatic classification method can replace process. Leukemia mainly classified into acute chronic types. current research work proposed computer-based application to classify disease. feature extraction stage, we use excellent physical properties improve diagnostic system's accuracy, based on Enhanced Color Co-Occurrence Matrix. study aimed at identification lymphocytic using microscopic images WBCs Virtual Neural Network (EVNN) classification. achieved optimum accuracy detection from WBC images. Thus, results establish superiority automated leukemia. values by terms sensitivity, specificity, error rate were 97.8%, 89.9%, 76.6%, 2.2%, respectively. Furthermore, system could predict disease prior images, probabilities also highly optimistic.