Chapter 1 - A cognitive approach to digital health based on deep learning focused on classification and recognition of white blood cells

Abstract

Cognitive computing is derived from self-learning systems employing techniques to accomplish particular human tasks intelligently, consisting of technologies that work in a complementary way, being the most advanced resources to have the generation of insights, capable of processing information similar to the way the human brain does. Machine learning (ML) works by accessing and analyzing data; it can arrive at intelligent conclusions and set standards, that is, it can learn. Artificial neural networks (ANNs) are a type of ML, composed of several nodes that interconnect in different branches, that learn by updating and expanding these ties and interconnections. Deep learning is derived from the ML method incorporating ANN in consecutive layers learning from data, useful to learn unstructured data patterns, emulating how the human brain operates, so machines can be trained to deal with problems and ill-defined issues. These intelligent learning technologies are often employed in image recognition, image classification, and even computer vision applications. The challenge associated with the research problem is in the accuracy of the approach due to the object of study (medical image of human blood smear fields) having several and different cell types present in it, which increases the degree of complexity in the differentiation, identification, and classification of cell subtypes. In this context, a cognitive approach was developed, employing the Jupyter Notebook together with Python, using the dataset of 12,500 medical images of human blood smear fields of nonpathological leukocytes, achieving an accuracy of 84.19%.