# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 2 Related Work  
### Conventional Machine Learning Algorithms  
[PERSON] et al. [16] developed a method for the segmentation and classification of white blood cells. For the preprocessing of microscopic blood images, they used histogram equalization and contrast enhancement. In addition, to extract white blood cells, they used the Otsu thresholding segmentation. The extracted features were given to K nearest neighbor (KNN) clustering to categorize the blood images into normal and blast cells. The proposed method was tested on 108 images of peripheral blood smears from a publicly available dataset. Another unsupervised segmentation method was developed by [PERSON] et al. [17] based on color scheme clustering for the classification of leukemia. A two-stage color segmentation approach based on fuzzy logic was used to differentiate leukocytes from other constituents of the blood. The authors extracted the features like the fractal dimension, shape, texture, and the signature of the contour. Their proposed method used 270 images for the classification of ALL. [PERSON] et al. [7] proposed an enhanced method for ALL recognition. In this method, they utilized fuzzy c-means clustering for the segmentation of lymphocytes. After feature extraction, they used principal component analysis (PCA) to reduce the features and fed them to the support vector machine (SVM) classifier to distinguish between normal and blast cells. They conducted their experiment on 958 images. [PERSON] et al. [18] proposed a method that isolates the leukocytes from a microscopic image and further distinguishes the nuclei and cytoplasm from the leukocytes. They trained different classification models and extracted shape, color, and texture features to determine which model is best suitable for leukemia classification. They used 368 images and tested several classifiers for classification purposes. Based on the classification accuracy they found that SVM with a Gaussian radial basis kernel outperforms other classifiers. [PERSON] et al. [19] developed an automatic detection algorithm for ALL based on local binary pattern (LBP) and geometric texture features. Their proposed model used a small dataset composed of 368 images for features extraction and these features are then fed to SVM for binary classification. [PERSON] et al. [20] proposed a method for automatic detection of leukemia from 108 microscopic images. They applied some filtering techniques to extract the important parts from images and then K-means clustering was used to produce the binary clusters. For classification purposes, they used an SVM classifier to distinguish between healthy and malignant cells. [PERSON] et al. [21] came up with a novel idea for leukemia detection using stained blood microscopic images. They used the median filter and histogram equalization for image preprocessing. For segmentation purposes, they used fuzzy c-means and SVM to classify the leukemia cells into normal and malignant classes. They used 19 microscopic images for leukemia cancer classification. Another segmentation and classification method proposed by [PERSON] et al. [22] involves the transformation of grayscale images to color space YCbCr. Then they computed the Gaussian distribution on each color and calculated a number of features like texture, scale, and morphology. These features are then given as input to the random forest classifier. They used a dataset of 105 images to train their model for WBCs classification. A significant drawback of these methods is they used small datasets to classify ALL cell images. A classifier trained on a small dataset is more vulnerable to overfitting and may not give an optimal result. The used datasets in the literature vary from 19 to 958 images. Another drawback is that these researchers used conventional machine learning algorithms (SVM, KNN, K-means clustering), which use handcrafted features that may not be an optimal set of features for classification. All these drawbacks can limit the final performance.