Received: 24 August 2018 / Revised: 11 May 2019 / Accepted: 21 June 2019 /
Published online: 19 July 2019
© Springer Science+Business Media, LLC, part of Springer Nature 201

Abstract

Diagnosis of microcytic hypochromia is done by measuring certain characteristics changes in the count of blood cell and related indices. Complete blood count test (CBC) is the common process for measuring these characteristic changes. However, the CBC test cannot be completely relied upon since there are chances of false diagnosis as these characteristics are also related to other disorders. In order to rectify the same, other expensive and lengthy tests need to be done which leads to further delay in accurate diagnosis and which may prove detrimental. In an attempt to find the solution to this problem, this paper proposes a method that uses feature fusion for classification of microcytic hypochromia. Feature fusion means combining blood smear image features extracted by the deep convolutional neural network (CNN) and clinical features from CBC test. This fused data-set is further used to predict microcytic hypochromia. After obtaining fused data set we use linear discriminant analysis (LDA) and principal component analysis (PCA) to reduce data set dimensions which further results in less computational overhead. To differentiate between microcytic hypochromia patients and normal persons, k-nearest neighbors (k-NN), support vector machine (SVM), and neural network classification models are used. In order to check the performance of the above model, various evaluation metrics are used. Results achieved from the proposed method reflect that fused data set can effectively improve the identification ratio with a very limited number of patients diagnostic images and clinical data (10 for normal and 10 for β-thalassemia) and feed-forward back-propagation neural network on this data set achieved accuracy, sensitivity, and specificity of 99%, 1.00, and 0.98, respectively. The limited number of patients reduces the system complexity and researcher’s time for getting data from different hospital to train the network.