Morphological Features Analysis for Erythrocyte Classification in IDA and Thalassemia
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 12
Abstract
Iron Deficiency Anemia (IDA) and Thalassemia is a common disease in the world population. In hospital routine, those diseases are being recognized based on level of hemoglobin in Complete Blood Count (CBC) result. Then, visual experts will conduct examination under the light microscope which is subjected to human error. In this research, we suggested a methodology via machine learning to classify and characterize erythrocyte related with IDA and Thalassemia. We employ some image pre-processing techniques on the blood smear images to enhance edges and reduce image noise such as gamma correction and morphological processing. Then, every single erythrocyte image will segment the background and foreground by using Otsu’s threshold method. Here, we have considered nine types of erythrocyte such as teardrop, echinocyte, elliptocyte, microcytic, hypochromic, target cell, acanthocyte, sickle cell and normal cell to be classified and portray based on their morphological features. Later, these 24 and 31 features from Hue’s moment, Zernike moment, Fourier descriptor and geometrical features are confirmed as potential features for each condition by calculating one-way ANOVA. Next, the rank of subset features is done based on their information gain value from maximum to minimum. Each of subset is separated by incremental of five features. Here, we compare the performance for each subset with five selected classifiers namely logistic regression, radial basis function network, multilayer perceptron, Naïve Bayes Classifier and Classification and Regression Tree. The best subsets from 31 features provide the highest result of classification with 83.5% accuracy, 83.5% sensitivity and 83.3% positive predictive value respectively via logistic regression compared to other classifiers. This study could be extended by using image dataset from other blood based disease for future work.
Authors and Affiliations
Izyani Ahmad, Siti Norul Huda Sheikh Abdullah, Raja Zahratul Azma Raja Sabudin
Intrusion Detection and Prevention Systems as a Service in Could-based Environment
Intrusion Detection and Prevention Systems (IDPSs) are standalone complex hardware, expensive to purchase, change and manage. The emergence of Network Function Virtualization (NFV) and Software Defined Networking (SDN) m...
Data fusion based framework for the recognition of Isolated Handwritten Kannada Numerals
combining classifiers appears as a natural step forward when a critical mass of knowledge of single classifier models has been accumulated. Although there are many unanswered questions about matching classifiers to real-...
Applying CRISPR-Cas9 Off-Target Editing on DNA based Steganography
Different from cryptography which encodes data into an incomprehensible format difficult to decrypt, steganography hides the trace of data and therefore minimizes attention to the hidden data. To hide data, a carrier bod...
Emotion Classification in Arousal Valence Model using MAHNOB-HCI Database
Emotion recognition from physiological signals attracted the attention of researchers from different disciplines, such as affective computing, cognitive science and psychology. This paper aims to classify emotional state...
Image Retrieval System based on Color Global and Local Features Combined with GLCM for Texture Features
In CBIR (content-based image retrieval) features are extracted based on color, texture, and shape. There are many factors affecting the accuracy (precision) of retrieval such as number of features, type of features (loca...