Wavelet scattering- and object detection-based computer vision for identifying dengue from peripheral blood microscopy

Liora Rosvin Dsilva, Shivani Harish Tantri, Niranjana Sampathila, Hilda Mayrose, G. Muralidhar Bairy, Sushma Belurkar, Kavitha Saravu, Krishnaraj Chadaga, Abdul Hafeez-Baig

Research output: Contribution to journalArticlepeer-review


Dengue fever infection is a global health concern. Early disease detection is crucial for averting complications and fatality. Characteristic morphological changes in lymphocytes can be observed on a peripheral blood smear (PBS) in cases of dengue infection. In this research, we have developed automated computer vision models for dengue detection on PBS images using two approaches: wavelet scattering transform (WST)-based feature engineering and classification and You Only Look Once (YOLO)-based deep transfer learning for object detection. In the former, Morlet wavelet scattering features extracted from lymphocytes were used as input for five shallow classifiers for image classification. Among these, the support vector machine achieved the best results of 98.7% accuracy using 10-fold cross-validation. In the latter, computer vision-enabled object detection was implemented using five YOLOv8 scaled variants. Among these, YOLOv8s and YOLOv8l attained identical best mean accuracy of 99.3% ± 1.4% across five independent experiments. Our results confirmed the feasibility and excellent diagnostic accuracy for both WST- and YOLOv8-enabled computer vision approaches for diagnosing dengue infection in PBS images. This research incorporates deep machine learning along with AI technology to enhance understanding and capabilities in automated Dengue diagnosis. The significance of this research extends to the broader domain of mosquito-borne illnesses. However, it is important to note that the findings are limited to the dataset used by the researchers.

Original languageEnglish
Article numbere23020
JournalInternational Journal of Imaging Systems and Technology
Issue number1
Publication statusPublished - 01-2024

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Software
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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