Optimized Feed Forward Neural Network for Microscopic White Blood Cell Images Classification

  • Shahd T. MohamedEmail author
  • Hala M. EbeidEmail author
  • Aboul Ella HassanienEmail author
  • Mohamed F. TolbaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 921)


Solving the slow convergence of the traditional neural network and searching for weights in Feed Forward Neural Network (FNN) is important to achieve the minimum training error. This paper presents an Optimized Feed Forward Neural Network (OFNN) for Microscopic white blood cell (WBC) images classification. Particle swarm optimization (PSO) and Gravitational Search Algorithm (GSA) are used to train feed forward neural network and to search for the weights of the FFN to achieve minimum error and high classification rate. The OFNN is used to classify the white blood cells into Agranulocytes that contains lymphocytes and monocytes cells and Granulocytes that contains neutrophils, eosinophils and basophils accurately. The OFNNs is trained using cells shape features of the segmented cells. The experimental results show that the obtained results are promising with classification accuracy being greater than 93% for all types.


Blood microscopic image Neural network Particle swarm Optimization Gravitational Search Algorithm 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt
  2. 2.Faculty of Computers and InformationCairo UniversityGizaEgypt
  3. 3.Scientific Research Group in Egypt (SRGE)GizaEgypt

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