## 2 Related Work  
[PERSON] et al. used a grey level matrix and feature bank to classify microcytic hypochromic anemic RBCs [4]. They got an accuracy of 96% with adaptive synthetic sampling and locality-sensitive discriminant analysis (LSDA). In [17], the authors proposed a technique for the automated identification of nucleated RBCs from the peripheral blood smear. They achieved an accuracy of 99.42% for detecting nucleated red blood cells using a multilevel threshold approach marker-controlled watershed algorithm. A total of 950 nucleated blood cells were examined from 50 blood smear images. In [18; 19; 20], the authors performed simultaneous segmentation of RBCs and WBCs and achieved with 94.8% and 97.2%, 97.45%, 93.43%, and 96.00% accuracies, respectively, using Marker-controlled watershed, modified SegNet model, and deep convolutional network. In [21; 22] the author proposed a novel technique for automated diagnosing of several blood diseases like AIDS, iron deficiency, a blood disorder, platelets, malaria, leukemia, and anemia. [PERSON] et al. [23] developed a statistical-based thresholding method for the segmentation of RBCs followed by the Fuzzy C-means using the ALL-IDB blood cell dataset. They performed single cell-based RBCs classification and achieved 96% accuracy. In [24], the authors applied the thresholding technique for the segmentation and detection of RBCs corners and recorded an accuracy of 87.9%. This method failed to detect the edges of the image accurately for the overlapped structure of cells. In [25], the author proposed a novel approach for splitting Rouleaux red blood cells from thin blood smears by applying distance transform and local maxima. Three evaluation parameters were used with evaluation rates of true positive rate (TPR) = 96%, error rate (ER) = 4% and accuracy (AC) = 98%. Semantic segmentation was applied by [26; 27] using SegNet architecture and VGG- for segmentation and counting of WBCs and RBCs cells element within the ALL-IDB1 dataset, respectively. A capsule network-based model was adopted [28; 29] to classify the blood elements in peripheral blood smear to enhance the incremental training procedure. In [30; 31; 32; 33], normal and abnormal RBCs have been detected based on form factor, perimeter, and area features with an accuracy of 94%, but fail with noisy images using random forest (RDF) and support vector machine (SVM) as classifiers. Malarial infected cells have been segmented by [34; 35; 36] with the help of [PERSON]'s method using the intensity threshold approach and CNN but failed to segment the overlapped structure of blood cells and blurry images. In [37; 38; 39], the authors targeted the shape of red blood cells to identify thalassemic and anemic RBCs using a multilayer perceptron. [37] used texture and color features to classify thalassemia microscopic images and obtained an accuracy of 93.77%. Watershed transform was applied by [34] to detect hypochromic and normochromic anemia with an accuracy of 96.7%. In [40], the author used CNN, KNN, and SVM to detect microcytic hypochromia using CBC and blood film features. In [41], the authors diagnoses three types of anemia, i.e., (1) iron deficiency anemia (IDA), (2) \(\alpha\)-thalasemia trait, and (3) \(\beta\)-thalasemia trait using weka software. The author used two parameters, (1) highest accuracy and (2) lowest mean absolute error, to evaluate the data mining technique. In [42], the authors analyzed the classification approaches, i.e., decision tree (KNN, CNN, SVM, Logistic regression) and association rule mining for statistical analysis of anemia. Random prediction (Rp)classification algorithms were used by [43] for the selection of anemia in pregnant women. The authors in [44; 45; 46] adopted classical approaches, i.e., WEKA, Naive Bayes, multilayer perception, and J48 algorithms to predict anemia types using CBC reports. The authors in [47] proposed a computer-aided system to diagnose blood disorders like anemia by classifying red blood cells.