A System for Acute Leukemia Cells Segmentation and Classification

Abstract

This research paper presents a system for the acute leukemia blast cells segmentation and classification. The research objective is to generate the features characterizing normal and infected cells. The proposed system consists of one segmentation method and one classification method of acute leukemia. The features extracted from the cell and adopted features are used as the input signals to the Multi Layer Perception (MLP) neural network classifier. The experimental results show that our proposed system is robust and effective in identifying acute leukemia blast cells.

Enhanced Pixel Based Urban Area Classification of Satellite Images Using Convolutional Neural Network

“…The structure of 1DCNN is simple. It is consisting of the few numbers of convolution layers followed by one or two fully connected layers as MultiLayers Perceptrons (MLP) [17]. This simple structure is suitable for simple pixels-based classification.…”