Bornoma Halima1, Farouk Hafsa Muazu1, Okonta Ehijesumuan1, Diadie Sow2, Ignace Djitog3, Ekpe Okorafor3, 1American University of Nigeria Yola, Nigeria, 2R&D Data Scientist, France, 3Nigerian British University, Nigeria
Bone marrow diseases are illnesses that affect the bone marrow of an individual. The bone marrow is a mesh-like organ situated inside the bones of a human being entirely in charge of producing blood and all blood components i.e. lymphocytes, erythrocytes, platelets and plasma. Any disease affecting the bone marrow affects the production of blood which can lead to loss of blood or cancers which eventually lead to death. This paper proposes a new web-based application integrated with convolutional neural networks algorithm, a machine learning approach, to automate an early diagnosis of bone marrow diseases without much hassle contrary to common manual processing which is exceedingly labor-intensive and costly. While the dataset was thoroughly examined, features that fit in with patient characteristics living with sickle cell disease in Nigeria were extracted to carry out the analysis. The dataset contains 11 classes of different bone marrow disease cell types, and a number of performance measuressuch as area under the curve (AUC), precision, and recall were generated and analyzed with the following results 98.38%, 87.12%, and 77.12% respectively. In terms of diagnosing sickle cell diseases with patients in Nigeria, the proposed model surpassed all existing learning models. The resulting model was saved in a specific file format then successfully imported into the developed web portal for instant analysis and wider access by authorized personnel.
Machine Learning, Convolutional Neural Networks (CNN), Bone-Marrow Disease, Hematology, Smear, Prediction, Web Application.
