OLUTUNMIDEA, Enoch Aduragbemi
Design and Implementation of an Automatic Acute Lymphoblastic Leukemia Diagnosis System - Dr. Kasali F.A. - Ibafo COMPUTER SCIENCE AND MATHEMATICS 2020 - ix; 67 dia, tables
Automatic Leukemia Diagnosis is a computer-aided approach to diagnosing leukemia. This work focuses on the diagnosis of Acute Lymphoblastic Leukemia (ALL) which accounts for 12% of all childhood and adult leukemias diagnosed in developed countries and for nearly 60% of those diagnosed in persons under 20 years of age (Pui, 2011). The relevance of this work is to find a way to reduce the over-reliance on medical specialist for the diagnosis of ALL. Machine Learning and Deep Learning algorithms are the current trends adopted for the purpose of medical diagnosis involving image analysis. This approach has been adopted by many other researchers for the purpose of diagnosing breast cancer (Poorti & Neetu, 2019), and prostate cancer (Janney, Christilda, Mary & Haritha, 2017), amongst others.
This project work would be achieved using Python 3.7. A number of Machine Learning models will be compared to find the best performing algorithm. The best performing algorithm will be implemented as an API in Python (Flask) and then hosted using Google Cloud Platform (GCP). The hosted API will then be consumed in an Android App for easy usage and diagnosis of ALL in medical facilities.
COMPUTER SCIENCE
Design and Implementation of an Automatic Acute Lymphoblastic Leukemia Diagnosis System - Dr. Kasali F.A. - Ibafo COMPUTER SCIENCE AND MATHEMATICS 2020 - ix; 67 dia, tables
Automatic Leukemia Diagnosis is a computer-aided approach to diagnosing leukemia. This work focuses on the diagnosis of Acute Lymphoblastic Leukemia (ALL) which accounts for 12% of all childhood and adult leukemias diagnosed in developed countries and for nearly 60% of those diagnosed in persons under 20 years of age (Pui, 2011). The relevance of this work is to find a way to reduce the over-reliance on medical specialist for the diagnosis of ALL. Machine Learning and Deep Learning algorithms are the current trends adopted for the purpose of medical diagnosis involving image analysis. This approach has been adopted by many other researchers for the purpose of diagnosing breast cancer (Poorti & Neetu, 2019), and prostate cancer (Janney, Christilda, Mary & Haritha, 2017), amongst others.
This project work would be achieved using Python 3.7. A number of Machine Learning models will be compared to find the best performing algorithm. The best performing algorithm will be implemented as an API in Python (Flask) and then hosted using Google Cloud Platform (GCP). The hosted API will then be consumed in an Android App for easy usage and diagnosis of ALL in medical facilities.
COMPUTER SCIENCE