Haemoglobin is measured via the traditional "fingerstick" test, which entails invasively drawing blood from the body. Traditional laboratory measures are accurate, but they have limitations such as time delays, patient discomfort, biohazard exposure, and a lack of real-time monitoring in critical situations. Researchers are paying close attention to non- invasive haemoglobin assessment since it can assist in identifying polycythemia, anemia, and a range of cardiovascular disorders earlier. This study looks at image-based research using a Deep Convolutional Neural Network for detecting haemoglobin levels. A diverse set of finger images with varying hemoglobin levels was employed to train the model. During testing, the model correctly classifies the haemoglobin level in a realistic condition.

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Deep Learning CNN non-invasive Haemoglobin detection DCNN