Malaria Cell-Image Classification using InceptionV3 and SVM
Marada Amrutha Reddy, Ganti Sai Siva Rama Krishna, T. T. Venkata Rayudu K. Vinay Kumar · Zenodo (CERN European Organization for Nuclear Research) · 2021
Malaria is a lethal illness spread by female anopheles mosquito bites. The bite carries parasites of the plasmodium group. In 2019, an estimated 229 million cases of malaria were reported globally, according to the WHO (World Health Organization). In the same year, an estimated 409 000 people died from malaria. If diagnosed early and treated promptly, the infection will not spread. The disease is diagnosed via a microscopic examination of the patient's blood sample. The sample is thinly spread as a smear, with the cell images serving as the visual criteria. Diagnosis is a time-consuming process that needs the assistance of an expert. To avoid incorrect findings caused by human error, various machine learning and deep learning methods have been developed. In this research, we built a transfer learning model using inception-v3 and SVM classifier. The achieved results show that a model with Inception-V3 as feature extractor and SVM classifier gave an accuracy score of 94.8 percent.