Potential leukaemia classification using ISURF-DLCNN from blood cell image

Abstract

There will be a total of 412,000 persons across the world who are diagnosed with leukaemia, with acute lymphoblastic leukaemia accounting for around 12% of all cases. As a consequence of this, leukaemia detection at an earlier stage has the potential to save the lives of millions of individuals. The identification of leukaemia using deep learning algorithms is the primary emphasis of this paper, along with blood cell counts. The photos are preprocessed using median filters, and then the K-means clustering (KMC) algorithm is used to split the data into its constituent parts. After that, the gathered features are fed into a deep learning convolutional neural network (DLCNN) in order to perform classification utilising an upgraded, speeded-up, and more robust feature descriptor (ISURF). The proposed technique achieved an accuracy rate of 99 percent while requiring a very low amount of effort, and it outperformed conventional approaches in terms of overall performance.