Leukemia diagnosis in blood slides using transfer learning in CNNs and SVM for classification

Abstract

Leukemia is a pathology that affects young people and adults, causing premature death and several other symptoms. Computer-aided systems can be used to reduce the possibility of prescribing inappropriate treatments and assist specialists in the diagnosis of this disease. There is a growing use of Convolutional Neural Networks (CNNs) in the classification and diagnosis of medical image problems. However, the training of CNNs requires a large set of images. To overcome this problem, we use transfer learning to extract images features for further classification. We tested three state-of-the-art CNN architectures and the features were selected according to their gain ratios and used as input to the Support Vector Machine classifier. The proposed methodology aims to correctly classify images with different characteristics derived from different image databases and does not require a segmentation process. We built a new database from the union of three distinct databases presented in the literature to validate the proposed methodology. The proposed methodology achieved hit rates above 99% and outperformed nine methods found in the literature.