Open AccessPrognostication of Acute Lymphocytic Leukemia (ALL) using Capsule Network Algorithm
Author(s) -
S. Melfi Rose,
J. Merlin Sheeba,
R. Prabaharan,
M. Bhuvaneshwari,
P. Subha Hency Jose
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1937/1/012021
Subject(s) - leukemia , bone marrow , acute leukemia , algorithm , white blood cell , medicine , acute lymphocytic leukemia , chemotherapy , oncology , lymphoblastic leukemia , computer science
A type of cancer that affects the blood-forming tissues in the body including lymphatic system and bone marrow is Leukemia. The second most commonly occurring acute leukemia is the acute lymphoblastic leukemia or acute lymphocytic leukemia (ALL). Around 25% of the cases are observed to be due to malignant T-cell precursors while the remaining 75% of cases is due to precursors of B-cell lineage. In general, response to chemotherapy, white blood cell count and age are the clinical factors that contribute towards risk stratification. However, in recent years it has been identified that genetic alterations have enabled between individual prognosis and recovery. Despite advancement in technology, chemotherapy using anthracycline, corticosteroids and vincristine serves to be the backbone therapy to treat this disease. In this proposed work, we have used a deep convolutional neural network to detect the presence of ALL accurately and based on the image screened, it is further categorized into one of the 4 subclasses. Using Capsule network algorithm (CapsNet), we have established 100% average sensitivity for ALL detection with a highest specificity of 99.56%, precision of 99.82% and accuracy of 99.36%. When compared with other similar methodologies, we have been able to accomplish higher accuracy without microscopic image segmentation using capsule network algorithm.