, 2026
APPLICATIONS OF NEURAL NETWORKS IN MEDICINE
We present and describe here the applications of neural networks for the detection and classification of white blood cells using microscopic images of peripheral blood smears. It was selected as an example the Acute Lymphoblastic Leukemia image database. This paper presents the Random Threshold Classifier. The Random Threshold Classifier includes the following blocks: Input data, Characteristic extractor, Encoder, Classifier, and Recognition class (ALL cells or Normal cells). To obtain input data we describe the images using brightness, contrast, and micro-contour orientation histograms. The extracted characteristics are presented to an encoder’s input of the neural network. The encoder generates a high-dimensional binary output vector, which is presented to the input of the neural classifier. The classifier’s output is the recognized class, which is either a healthy cell or an Acute Lymphoblastic Leukemia-affected cell. The proposed Random Threshold Classifier achieved a recognition rate of 98.3 % when the data has partitioned in proportion: 80 % for training set and 20 % for testing set (pp.33-41).
Keywords:RTC neural classifier, Image processing, Cell analysis, Detection of white blood cells, Leukemia classification, Artificial Intelligence
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