Intelligent algorithm for detection of dengue using mobilenetv2‐based deep features with lymphocyte nucleus. Issue 4 (30th November 2021)

• Record Type: Journal Article
• Authors: Mayrose, Hilda; Sampathila, Niranjana; Bairy, G. Muralidhar; Belurkar, Sushma; Saravu, Kavitha; Basu, Akash; Khan, Saman
• Is Part Of: Expert systems. Volume 40:Issue 4(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-30
• Subjects: Deep features -- Dengue -- Digital pathology -- Lymphocyte -- MobileNetV2 -- ReliefF -- SVM classifier; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12904
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: Dengue is a vector‐borne disease that is highly endemic in countries located in tropical regions. It can cause severe complications and can even lead to death in the case of delayed diagnosis. Detection of dengue is done by manually examining the platelets and lymphocytes in Leishman's stained peripheral blood smear (PBS) images. PBS examination is considered the gold standard for diagnosing various haematological disorders. However, manual analysis of the PBS is labour‐intensive, tedious, and time‐consuming, requiring a skilled and experienced haematologist. Today, soft computing methods and artificial intelligence have made their way into every science and technology branch. One such area which has adopted this approach is digital pathology, for automatically identifying and diagnosing various diseases. The main objective of this work was to design an intelligent algorithm to classify normal and dengue patients with the help of digital microscopic blood smear images. A total of 94 normal and dengue‐infected PBSs were acquired at a magnification of 100×. Grey‐level segmentation based on Otsu's thresholding was used for the segmentation of the nucleus of lymphocytes. Distinct features from the nucleus that differentiated infected cells from normal were extracted using a pre‐trained MobileNetV2 network and local binary pattern. Significant features were selected using the ReliefF algorithm. Subsequently, these features were fed to the support vector machine (SVM) classifier. Our proposed system gave an accuracy, sensitivity, and specificity of 95.74%, 98.14%, and 92.50%, respectively. Hence, the developed intelligent model with deep and hand‐crafted features can be valuable for dengue diagnosis.