# A Novel Deep Learning Segmentation and Classification Framework for Leukemia Diagnosis
## 1 Introduction
### Related Work  
Recently, Computer Vision (CV) was deployed in various leukemia models to identify and classify tumors.  
[PERSON] et al., in [1], developed a model to detect and categorize leukemia based on deep learning technology. A Generative Adversarial Network (GAN) was used for feature extraction purposes. In addition, a Generative Adversarial System (GAS) and the Principal Component Analysis (PCA) method were deployed on the extracted characteristics to distinguish suitable types of leukemia. Furthermore, the authors deployed some image preprocessing methods to segment the detected blood cells. The developed method achieved 99.8%, 98.5%, 99.7%, 97.4%, and 98.5% for accuracy, precision, recall, F-score, and DSC, respectively. These achieved results came from applying the model to one dataset. In contrast, the proposed model utilized the newly implemented segmented technique and the newly developed UNET model to detect and categorize leukemia according to the extracted features. The presented method used three datasets to test the algorithm and achieve an acceptable range of outcomes between 97% and 99%. The obtained findings imply that the model can be used by any hospital or healthcare provider to support pathologists and physicians in their diagnosis of leukemia properly and accurately. The proposed model produces better results than the developed method in [1] in some cases, as its accuracy reached 99% when the number of iterations and epochs increased.  
The authors in [2] implemented a method to discover long noncoding RNA using a competitive network. This network was developed on endogenous RNA and utilized on a dataset that had long noncoding RNAs and mRNAs. In addition, the authors validated their outcomes using a reverse transcription quantitative real-time approach. Unfortunately, the authors provided no information about the achieved accuracy or any other metric, while the proposed method achieved 97.82% accuracy using the new segmentation technique.  
In [3], [PERSON] et al. proposed a method to detect leukemia based on a histogram threshold segmentation classifier. This method worked on the color and brightness variation of blood cells. The authors cropped the detected nucleus using arithmetic operations and an automated approximation technique. In addition, the active contour method was utilized to determine the contrast in the segmented white blood cells. The authors achieved 98% accuracy and reached more than 99% when combined with other classifiers on a single dataset. In contrast, the proposed algorithm utilized three datasets and achieved nearly an average of 98% accuracy. This accuracy increased to nearly 98.9% when enhancing the number of iterations and epochs as well.  
The authors in [4] implemented a model to discover malignant leukemia cells using a deep learning model. The authors used a Convolutional Neural Network (CNN) to spot ALL and AML. In addition, two blocks of the CNN model were hybridized together. The algorithm was evaluated on a public dataset that contained 4150 blood smear images. In addition, transforming the color images into grey ones was deployed to assist the model in segmentation purposes. Five different classifiers were utilized to evaluate the model to achieve 89.63% accuracy, which is considered to be a low outcome. In contrast, the presented approach in this article used new segmentation and classification methods to spot and categorize four subcategories of leukemia. Four datasets were utilized to attain 97.82% accuracy, which is better than the obtained outcomes in [4].  
[PERSON] et al., in [6], implemented a model to categorize leukemia using the VGG-16 CNN model when applied to a single dataset of nearly 700 images. This model classified only two types, which were ALL and AML, and attained nearly 98% accuracy, which is less than what the proposed algorithm obtained on four datasets.  
Table 1 offers a summary of what has been implemented to detect and categorize leukemia.