Abstract
AbstractDiagnostic imaging is in itself an important technological evolution, which represented an advance in the way diseases are diagnosed, originating from the digital age in diagnostic medicine, assessing the current digital transformation experienced by health organizations. It allows, with the use of Artificial Intelligence (AI) in diagnostic medicine, in order to speed up and make the diagnosis of patients more accurate, anticipating medical outcomes, capable of improving internal processes of health services, allowing efficiency gains in diagnostic investigation and reduction of waste of resources. In this context, Blood is investigated through blood count through manual and/or automated procedure, however, in search of innovations in the field of medicine, this chapter presents the development of a Deep Learning framework for classifying white blood cell subtypes in digital images achieving criteria for efficiency and reliability, while making the methodology more accessible to less favored populations. Reflecting on laboratory medical exams generally present costs that are inaccessible to populations of underdeveloped and developing countries. Faced with this, it is of enormous value to create tools that facilitate the procurement of medical reports with high reliability and low cost. For this, it was implemented in python employing Jupyter notebook and essay conducted utilizing a dataset containing 12,500 digital images of human blood smear fields consisting of non-pathological leukocytes and as a result, the accuracy of the approach was 85.72% evidencing the high reliability of the proposed methodology. Therefore, the proposal is evaluated as an accurate, reliable, and inexpensive method that can be utilized as a third feasible proceeding for blood count in often underprivileged populations of underdeveloped and developing countries.KeywordsHealthcareBiomedical signalsDigital imageCognitive computingHealthcareArtificial intelligenceDeep learningPythonCNNImage processingHealthcare informaticsCognitive modelsErythrocytesLeukocytesHealthcare dataCognitive healthcare
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