Identification of Anemia and Its Severity Level in a Peripheral Blood Smear Using 3-Tier Deep Neural Network

• Published in: Applied sciences 2022-05, Vol.12 (10), p.5030
• Main Authors: Shahzad, Muhammad, Umar, Arif Iqbal, Shirazi, Syed Hamad, Khan, Zakir, Khan, Asfandyar, Assam, Muhammad, Mohamed, Abdullah, Attia, El-Awady
• Format: Article
• Language: English
• Subjects: Accuracy; Anemia; Automation; Blood; Blood cells; Blood diseases; Cell differentiation; Cell size; Classification; Cognition; convolutional neural network; Datasets; Discriminant analysis; Disease detection; disease severity detection; Erythrocytes; Hemoglobin; Iron; Leukemia; machine learning; Medical imaging; Morphology; Neural networks; Peripheral blood; red blood cell; Research facilities; Support vector machines; Watersheds
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12105030

The automatic detection of blood cell elements for identifying morphological deformities is still a challenging research domain. It has a pivotal role in cognition and detecting the severity level of disease. Using a simple microscope, manual disease detection, and morphological disorders in blood cells is mostly time-consuming and erroneous. Due to the overlapped structure of RBCs, pathologists face challenges in differentiating between normal and abnormal cell shape and size precisely. Currently, convolutional neural network-based algorithms are effective tools for addressing this issue. Existing techniques fail to provide effective anemia detection, and severity level prediction due to RBCs’ dense and overlapped structure, non-availability of standard datasets related to blood diseases, and severity level detection techniques. This work proposed a three tier deep convolutional fused network (3-TierDCFNet) to extract optimum morphological features and identify anemic images to predict the severity of anemia. The proposed model comprises two modules: Module-I classifies the input image into two classes, i.e., Healthy and Anemic, while Module-II detects the anemia severity level and categorizes it into Mild or Chronic. After each tier’s training, a validation function is employed to reduce the inappropriate feature selection. To authenticate the proposed model for healthy, anemic RBC classification and anemia severity level detection, a state-of-the-art anemic and healthy RBC dataset was developed in collaboration with Shaukat Khanum Hospital and Research Center (SKMCH&RC), Pakistan. To evaluate the proposed model, the training, validation, and test accuracies were computed along with recall, F1-Score, and specificity. The global results reveal that the proposed model achieved 91.37%, 88.85%, and 86.06% training, validation, and test accuracies with 98.95%, 98.12%, and 98.12% recall F1-Score and specificity, respectively.