Leukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automatic method must ...
详细信息
Leukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automatic method must be employed. This paper proposes the Salp Swarm integrated dolphinecholocation-based Support Vector Neural Network (SSDE-SVNN) classifier to detect leukemia in its early stages. The pre-processed blood smear image is subjected to segmentation with the use of LUV transformation and Adaptive thresholding. The features, such as area, shape, texture, and empirical mode decomposition are extracted from the segments. The proposed classifier is used for the counting of blast cells based on the extracted features. The accuracy, specificity, and sensitivity of the proposed classifier are obtained as 0.97, 0.97, and 1, respectively, and the Mean Square Error (MSE) is noted as 0.1272.
ABSTRACTABSTRACTLeukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automa...
详细信息
ABSTRACTABSTRACTLeukemia is the abnormal and uncontrolled development of the white blood cells, known as leukocytes, in the blood. The manual methods used for counting the blast cells have some demerits, and so automatic method must be employed. This paper proposes the Salp Swarm integrated dolphinecholocation-based Support Vector Neural Network (SSDE-SVNN) classifier to detect leukemia in its early stages. The pre-processed blood smear image is subjected to segmentation with the use of LUV transformation and Adaptive thresholding. The features, such as area, shape, texture, and empirical mode decomposition are extracted from the segments. The proposed classifier is used for the counting of blast cells based on the extracted features. The accuracy, specificity, and sensitivity of the proposed classifier are obtained as 0.97, 0.97, and 1, respectively, and the Mean Square Error (MSE) is noted as 0.1272.
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