Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms.

Blood is composed of white blood cells, red blood cells, and platelets. Segmentation of the blood smear cells and extraction of features of the cells is essential in the field of medicine. Acute lymphoblastic leukemia is a form of blood cancer caused due to the abnormal increase in the production of immature white blood cells in the bone marrow. It mostly affects the children below 5 years and adults above 50 years of age. Due to the late diagnosis and cost of the devices used for the determination, the mortality rate has increased drastically. Flow cytometry technique that performs automated counting fails to identify the abnormal cells. Manual recount performed using hemocytometer are prone to errors and are imprecise. The proposed work aims to survey different computer-aided system techniques used to segment the blood smear image. The primary objective here is to derive knowledge from the different methodologies used for extracting features from white blood cells and develop a system that would accurately segment the blood smear image by overcoming the drawbacks of the previous works. The objective mentioned above is achieved in two ways. Firstly, a novel algorithm is developed to segment the nucleus and cytoplasm of white blood cell. Secondly, a model is built to extract the features and train the model. The different supervised classifiers are compared, and the one with the highest accuracy is used for the classification. Six hundred images are used in the experimentation. InfoGainAttributeEval and the Ranker Search method are used to achieve the feature selection which in turn helps in improvising the classifier performance. The result shows the classification of the acute lymphoblastic leukemia into its three respective categories namely: ALL-L1, ALL-L2, ALL-L3. The model can differentiate between a normal peripheral blood smear and an abnormal blood smear. The extracted feature values of a cancerous cell and a normal cell are also shown. The performance of the model is evaluated using the test images stained with various stains. The proposed algorithm achieved an overall accuracy of 98.6%. The promising results show that it can be used as a diagnostic tool by the pathologists. Graphical abstract.