International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Bloodless Haemoglobin level Detection using Deep Convolution Neural Network Sanketi Raut1, Samarth Soni2, Samkit Shah3, Ishan Barot4, Pranav Upadhyay5 1
Professor, Dept of Information Technology, Universal College of Engineering, Maharashtra, India Student, Dept of Information Technology, Universal College of Engineering, Maharashtra, India
2,3,4,5
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Abstract - The classic "fingerstick" test, which involves
undergoing cancer therapy are given a haemoglobin test to determine the success of their treatment. one of the required steps in making judgments about blood transfusions is a haemoglobin test. The classic "fingerstick" test, which includes invasively extracting blood from the head, quantifies haemoglobin. After cleaning the finger, the health professional will pierce the tip with a tiny needle (or lancet) to collect the blood. collecting a blood sample is only painful for a brief time and can feel like a short pinprick. even though traditional laboratory measurements are dependable, it has some drawbacks, i.e., time delays, patient annoyance, unsafe exposure, and the shortage of actual surveillance in critical situations.
invasively extracting blood from the body, is used to quantify haemoglobin (Hb). Although traditional laboratory measurements are reliable, they have their own drawbacks, such as time delays, patient inconvenience, biohazard exposure, and the lack of real-time surveillance in crucial circumstances. Through non-invasive hemoglobin assessment (SpHb) researchers can easily help diagnose polycythemia, anaemia, and a variety of cardiovascular illnesses and thatβs why it has gotten a lot of attention. Deep Convolutional Neural Network (DCNN) based picture research for haemoglobin level detection is investigated in this work. We used a heterogeneous evaluate accurately with various haemoglobin levels to train. It displays the concentration of haemoglobin in a realistic situation during testing. Keywords: Deep Learning, CNN, non-invasive, Haemoglobin detection, DCNN
2. LITERATURE SURVEY 1.INTRODUCTION
In all vertebrates, haemoglobin (Hb) is a metalloprotein found in red blood cells (RBC) that contains iron and transports oxygen [1]. Hb deficiency has been linked to premature births, low birth weight, and maternal death [2]. According to the WHO ( World Health Organization ), there is a total of 24.8 % shortage of Hb in the whole world. Hb in the blood can be calculated using a variety of ways. The CBC (Complete Blood Count) is a clinically accepted method of taking uncomfortable, time-consuming, and expensive blood. As a result, it's challenging to diagnose continuously, especially in pregnant women and ICU patients, when a Hb level test is required regularly. Men's blood sugar levels should be between 13.5 and 17.5 gm/dl, while women's blood sugar levels should be between 12 and 15.5 gm/dl. However, less than 10 gm/dl is considered a deficiency of Hb in clinical terms. A total of 104 data points were obtained from individuals for this study. Eighty-one data points are picked from the complete dataset to train the model, while 23 data
Haemoglobin (Hb) is a complex biological molecule production of red blood cells that transports oxygenated blood to the body's organs and tissues while also transporting away carbon dioxide heart to the lungs. A sufficient haemoglobin level must always be maintained to ensure appropriate tissue oxygenation, screen for and help identify diseases that impact Red Leukocytes, and assess the degree and classification of anaemia or polycythemia. The determination of haemoglobin is one of the most routine laboratory examinations. Testing is performed during a systematic health assessment when a person shows signs or symptoms of a disorder that affects red blood cells, such as anaemia or associated characteristics. when a person is diagnosed with continuous bleeding problems or chronic anaemia, this test may be repeated oftentimes or frequently. patients
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