GFNB: Gini index-based Fuzzy Naive Bayes and blast cell segmentation for leukemia detection using multi-cell blood smear images.

Abstract

The blood cell counting and classification ensures the evaluation and diagnosis of a number of diseases. The analysis of white blood cells (WBCs) permits us to detect the acute lymphoblastic leukemia (ALL), a type of blood cancer that causes fatality when untreated. At present, the morphological analysis of blood cells is performed manually by skilled operators, which holds numerous drawbacks. The manual techniques for leukemia detection are time-consuming and show less accurate results. Hence, there is a need for an automatic method for detecting leukemia. In order to overcome the demerits associated with the manual methods of counting and classifying, an automatic method of blast cell counting and leukemia classification is progressed. This paper proposes a leukemia detection method, using the Gini index-based Fuzzy Naive Bayes (GFNB) classifier that is the integration of Gini index and Fuzzy Naive Bayes classifier. Initially, the input multi-cell blood smear image is subjected to pre-processing, and the blast cell is segmented using the adaptive thresholding. Then, the blast cells are counted using the proposed classifier so as to decide the presence of leukemia for the effective diagnosis. Experimental analysis using the ALL-IDB1 database confirms that the proposed method operates better than the existing methods in terms of accuracy, specificity, and sensitivity that are found to be 0.9591, 0.9599, and 1, respectively. The experimental results reveal that the proposed method is reliable and accurate. Also, the proposed system can help the physicians to improve and speed up their process.Graphical abstract Leukemia is caused by the excess production of the immature leucocytes in the bone marrow that expose the human body to lose the tendency to fight against the diseases. Leukemia classification is highly needed as in the later stage, failure of the diagnosis steps may lead to the death of the person. Moreover, some countries do not have any study against the diagnosis steps of leukemia and it highly exists among the low-income people. In order to analyze the type of leukemia and to provide an effective diagnosis strategy, the paper presents a fast and highly accurate classification method. The main aim of the paper is to propose a method to perform the leukemia classification through the segmentation and classification of the WBC cells using the multi-cell blood smear images. The major steps involved in the leukemia classification are pre-processing, segmentation, feature extraction, and classification. The input blood smear image is enhanced in the pre-processing step and the pre-processed image is subjected to segmentation using the LUV color transformation and Adaptive Thresholding strategy. The features are extracted from the individual segments and they are presented to the classifier for the classification. The features extracted are shape, texture, and count of the blast cells, for which the grid-based shape extraction, local gradient pattern (LGP)-based texture features, and pixel threshold-based counting of the blast cells are employed. The proposed classifier is developed using the Gini index and Fuzzy Naive Bayes classifier.