Automated Anemia Screening via Nail Image Analysis Using Fast RCNN

In the modern world, the health sector is quickly moving to digital platforms due to its practicality, ease of use, and affordability. According to the World Health Organization, anemia will continue to be a major global health issue in 2023, affecting 571 million women and 269 million young children. Anemia is still very common, especially in low- and middle-income countries. Photo plethysmography (PPG), a non-invasive technique for detecting anemia, has focused on selecting the appropriate light sources and wavelengths. Numerous methods, including data processing, photo detector design, and the use of lights with different spectra, can be used to detect anemia. Cost and excessive power use are the primary disadvantages. The proposed method includes a novel nail image-based anemia diagnosis approach to address the aforementioned issues. In order to detect leukonychia, which is characterized by the presence of white spots or streaks that are indicative of iron deficiency anemia, the primary goal is to suggest an improved method for analysing pictures of a person's nails. For example, this method makes use of state-of-the-art convolutional neural network architectures, such as Fast RCNN and RCNN. We show that Fast RCNN outperforms RCNN in terms of accuracy, reaching 92%. A significant step in the right direction toward enhancing anemia-related healthcare outcomes worldwide is the suggested strategy, which blends state-of-the-art technology with medical diagnostics. Additionally, the system creates opportunities for further improvements, such as adding real-time monitoring features and growing dataset which include a wider range of demographic traits.