# An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
## 2 Related Work
### Deep Learning-Based Methods  
With the emergence of deep neural networks, we can achieve better performance in computer vision. A deep neural network can automatically extract task-specific features using two-dimensional convolutional filters to overcome the problem of predefined features. Deep neural networks are widely utilized in the field of computer vision, especially for medical image analysis, such as disease classification [23], localization [24], detection [25], registration [26], and segmentation [27; 28]. However, the performance of a deep neural network depends on the size of the dataset, but unfortunately, it is hard to get a larger dataset for medical image analysis. In order to deal with small dataset transfer, learning strategy and data augmentation can be used to overcome the problem of the limited dataset. [PERSON] et al. [29] used a pre-trained AlexNet and fine-tuning to identify ALL subtypes on a private dataset composed of 330 images. [PERSON] et al. [30] proposed a deep CNN to classify all four subtypes of leukemia. To avoid training from scratch they used a pre-trained AlexNet to perform binary classification on 368 images. A classification model focused on both transfer learning and deep learning was proposed by [PERSON] et al. [31] for WBC classification. The proposed approach began with preprocessing of the dataset and then employed transfer learning for feature extraction. Finally, the Inception and ResNet were used to perform WBCs classification. Their proposed model used 352 images for WBC classification. [PERSON] et al. [32] proposed a CNN model that recognizes all subtypes of leukemia. Initially, they used 903 images for classification of ALL subtypes. They also used seven distinct data augmentation techniques to enhance the dataset size. In comparison to CNN, they also investigated other machine learning algorithms like decision tree (DT), SVM, Naive Bayes, and KNN and found that CNN outperforms all other methods. [PERSON] et al. [33] used SVM to identify spatial features and then suggested a neural network with marker-based learning vector quantization to detect and classify ALL cells. They used 24 samples (16 out of 27 ALL patients and 8 normal samples with no clinical history of leukemia) to train their proposed method for ALL diagnosis. [PERSON] et al. [34] proposed a new method to identify ALL and its subtypes using a CNN network called ConVNet. They evaluated their model with other machine learning algorithms such as SVM, multilayer perceptron (MLP) and random forest. They used two types of datasets, one for ConVNet and the other for feature extraction. The overall dataset consists of 363 images. Again, here is the problem of a limited number of images as many researchers used a tiny dataset consisting of 24 to 903 images. The performance results of such a small dataset cannot be considered an optimal performance indicator for medical image analysis. On the other hand, classification becomes extremely difficult when dealing with small datasets, which results in an incorrect and biased classification model [22]. In addition, using pre-trained models for classification needs to adjust the input image's size according to the predefined input shape of the networks, which can alter the morphology of ALL cell images [35]. It is difficult to classify ALL cell images because these leukocytes are challenging to detect due to their high intraclass homogeneity and low interclass separability. Furthermore, they used transfer learning or fine-tuning of deep neural networks for ALL classification. A model trained on non-medical images and used for medical image classification may not achieve optimal results because the cell image features may differ significantly from natural images. A model trained from scratch on cell images may converge to a better solution. Currently some researchers used a larger dataset for the classification of ALL. [PERSON] et al. [36] proposed an aggregated-based deep learning-based model for the classification of ALL.