## 2 Related Work
In addition, they used a sparse representation classifier to characterize the extracted features [24]. Initially, the researchers [18] improved the visual information quality of each input image using various pre-processing and augmentation techniques. They suggested a hybrid architecture built on pre-trained CNN models (MobileNet and VGG16) for disease classification. The research has also converted RGB images into a CMYK color system. After that, images with updated color spaces are supplied to histogram equalization and thresholding. The method of segmentation was [PERSON]'s algorithm. Before proceeding with [PERSON]'s algorithm, intensity values are generated by histogram on the image dataset. After that, values of thresholding were produced by [PERSON]'s algorithm. To classify microscopic images, They used K-NN, SVM-RBF, SVM-L, SVM-P, NB-G, NBK, and TREE in different scenarios, but K-NN achieved the best classification accuracy [25]. Some researchers have changed the color space from RGB to Greyscale, and further, they implemented median filtration to remove noise [26; 27]. Global thresholding is applied to the blood samples to extract different objects at different pixel intensities. For classification, they tested multiple classifiers on microscopic images. They used support vector machines (SVM), smooth support vector machines (SSV), k-nearest neighbor, probabilistic neural network (PNN), and adaptive neuro-fuzzy inference (ANFI) system [26]. Researchers have changed the color space of resized RGB images to CMYK and applied the L*a*b color system. In segmentation, writers used two color space systems for the segmentation: CMYK and L*a*b of white blood [28]. After acquiring the sample images, the author transforms color space from RGB to CIE L*a*b, which helps reduce the color dimensions. Segmentation Sequential Neural Network was applied to increase the system's performance. The authors proposed a Sequential Neural Network Architecture (SNN) because this technique is based on accuracy and time efficiency. Their proposed methodology [29] was based on two stages; however, the authors could not perform both stages due to a lack of input images. Feature selection and an extreme learning machine were used to classify WBCs (ELM). To expand the number of images, data augmentation is conducted initially, followed by using a unique contrast stretching technique known as pixel stretch (PS). Color and grey level size zone matrix (GLSZM) features are generated from PS images and fused into a single vector based on a high similarity level in the next phase. However, there are a few redundant features that impact classification performance. A maximum relevance probability (MRP) based feature selection strategy is used to solve this challenge. All features with the greatest importance are added to ELM, and the process is repeated until the error rate is zero. Cubic SVM is used to classify the final selected features and attained the highest accuracy of 96.60 percent [30]. An automated method for detecting nuclei and extracting leukocytes from peripheral blood smear images with colour and illumination variations is described in the proposed study. Nuclei are detected using arithmetic and morphological processes, while leukocytes are detected using the active contours method. The findings show that the suggested approach successfully detects nuclei and leukocytes with Dice scores of 0.97 and 0.96, respectively. The method's overall sensitivity is around 96 percent [6]. Scientists developed a strategy for classifying ALL into subtypes and reactive bone marrow in stained bone marrow pictures in this study. To get reliable classification results, the model is trained on bone marrow images using robust segmentation and deep learning approaches with the convolutional neural network. The findings of this experiment were compared to the results of different classifiers such as Naive Bayesian, KNN, and SVM. The proposed technique demonstrated 97.78 percent accuracy in diagnosing Acute Lymphoblastic Leukemia and its subtypes [31].