Abstract:

One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on mea-suring the level of haemoglobin and the classification of RBCs based on a microscopic examination in blood smears. This paper presents a proposed algorithm for detecting and counting three types of anaemia-infected red blood cells in a microscopic coloured image using circular Hough transform andmorphological tools. Anaemia cells include sickle, elliptocytosis, microsite cells and cells with unknownshapes. Additionally, the resulting data from the detection process have been analysed by a prevalentdata analysis technique: the neural network. The experimental results for this model have demonstratedhigh accuracy for analysing healthy/unhealthy cells. This algorithm has achieved a maximum detectionof approximately 97.8% of all cells in 21 microscopic images. Effectiveness rates of 100%, 98%, 100%, and99.3% have been achieved using neural networks for sickle cells, elliptocytosis cells, microsite cells andcells with unknown shapes, respectively.

Introduction:

The general components of human blood are plasma, white blood cells (WBCs), red blood cells (RBCs) and platelets. RBCs com-prise approximately 40% of blood volume. WBCs are smaller in volume but larger in size than RBCs. Plasma is the fluid component that contains melted salts and proteins. Platelet cells are similar particles but are smaller than WBCs and RBCs (Xia and Wu, 2015;Biradar et al., 2015; Deligiannidis and Arabnia, 2014).Anaemia is a type of red blood cell disorder that is usually caused by a lack of mineral iron in the blood. The human body needs iron to produce the iron-rich protein haemoglobin, which helps red blood cells carry oxygen from the lungs to the remainder of the body(Elsalamony, 2014; Das et al., 2013). This disease occurs when the blood has a lower than normal number of red blood cells (RBCs) oran insufficient amount of haemoglobin. RBCs are located inside the large bones of the body in the spongy marrow. The main function of marrow is to renew red blood cells, which continuously replace sold red blood cells. Normal RBCs die after they have lived in the bloodstream for 120 days. Their jobs include carrying oxygen and removing carbon dioxide (a waste product) from the body (Lam,2015).RBCs are disc-shaped and can easily move through blood ves-sels. Elliptocytosis is a well-known type of anaemia. Historically, this disease was described in 1904 and recognized as a hereditary condition in 1932. The medical determination of hereditary ellip-tocytosis is difficult. The incidence of this disease ranges between three and five cases per 10,000 in the USA, whereas an estimated60–150 cases per 10,000 of African and Mediterranean natives and1500–2000 per 10,000 cases of Malayan natives have been documented (Lee and Chen, 2014).In sickle-cell anaemia, which is a serious disorder, the bodycreates a crescent shape of red blood cells. These sickle cells contain abnormal haemoglobin, which is referred to as sickle haemoglobin or haemoglobin S; it helps cells to develop a crescent shape. The absence of a polar amino acid encourages then on covalent combination of haemoglobin in a low-oxygen environment, which distorts the red blood cells into a sickle shape and decreases their elasticity. Biochemically, the low-oxygen environ-ment causes a chain of neighbouring haemoglobin molecules to hook together and block blood flow in the blood vessels of the limbs and organs, which become rigid and polymerized. Low blood flow can cause pain, organ damage, and increase the probability of disease.

Objective:

Classifying erythrocytes (red blood cells) using machine learning (ML) serves various objectives within medical diagnostics, research, and healthcare. The primary objective of employing ML for erythrocyte classification is to automate the process of identifying and categorizing different types of red blood cells based on their morphology, size, shape, and other characteristics present in microscopic images or digital representations.

Automated Diagnosis: ML models can assist medical professionals in diagnosing blood disorders and diseases by accurately classifying erythrocytes. This automation reduces the time and effort required for manual inspection, allowing for faster diagnosis and treatment decisions.

Improved Accuracy: ML algorithms can achieve a high level of accuracy in erythrocyte classification, surpassing the capabilities of manual analysis. By leveraging advanced pattern recognition techniques, ML models can identify subtle differences in erythrocyte morphology that may not be easily discernible to the human eye.

ML models can detect and classify abnormal erythrocytes, such as sickle cells, spherocytes, and target cells, which are indicative of various blood disorders and diseases. Early detection of these abnormalities through automated analysis can facilitate timely intervention and management of health conditions