Examining blood microscopic images for leukemia is necessary when expensive equipment for flow cytometry is unavailable. Automated systems can ease the burden on medical experts for performing this examination and may be especially helpful to quickly screen a large number of patients. We present a simple, yet effective classification approach using a ResNeXt convolutional neural network with Squeeze-and-Excitation modules. The approach was evaluated in the C-NMC online challenge and achieves a weighted F1-score of 88.91 on the test set. Code is available at this https URL
@cite_9: 
@cite_13: Leukemia is a fatal disease of white blood cells which affects the blood and bone marrow in human body. We deployed deep convolutional neural network for automated detection of acute lymphoblastic ...
@cite_17: Leukemia is a worldwide disease. In this paper we demonstrate that it is possible to build an automated, efficient and rapid leukemia diagnosis system. We demonstrate that it is possible to improve the precision of current techniques from the literature using the description power of well-known Convolutional Neural Networks (CNNs). We extract features from a blood smear image using pre-trained CNNs in order to obtain an unique image description. Many feature selection techniques were evaluated and we chose PCA to select the features that are in the final descriptor. To classify the images on healthy and pathological we created an ensemble of classifiers with three individual classification algorithms (Support Vector Machine, Multilayer Perceptron and Random Forest). In the tests we obtained an accuracy rate of 100 . Besides the high accuracy rate, the tests showed that our approach requires less processing time than the methods analyzed in this paper, considering the fact that our approach does not use segmentation to obtain specific cell regions from the blood smear image.
@cite_9 classify ALL subtypes on a private dataset of 330 images using a pre-trained AlexNet and fine-tuning. Shafique and Tehsin @cite_13 classify ALL subtypes on ALL-IDB augmented with 50 private images, also using a pre-trained AlexNet and fine-tuning. @cite_17 classify ALL on ALL-IDB using a number of different pre-trained CNNs as fixed feature extractors. From these CNN features the most informative ones are selected using PCA and finally classification is performed with an ensemble of SVM, MLP, and random forest.