Document Type : Original Article

Authors

• Ahmad nasrollahpour ¹
• Mohammad Khanabadi ²
• Toktam Khatibi ²

¹ Quchan University of Technology, Tarbiat Modares University

² Department of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Abstract— Detecting white blood cells (WBC) in microscopic images is essential in medical diagnosis. Manual analysis of these images is time-consuming and has a high error rate. Using object detection for WBCs detection with deep convolutional neural networks (CNN) can be considered a practical and effective solution. In this study, a CNN model is proposed to classify these images. In order to achieve optimal training performance, CNNs have many hyperparameters, such as dropout rate, number of hidden units in each hidden layer, activation function, loss function and optimizer, which need to be optimized. Therefore, a hyperparameter optimization approach based on a genetic algorithm is suggested, which can then be used to select the best combination parameters to improve accuracy and efficiency in detecting white blood cells in microscopic images. This new approach is significant and flexible for medical technicians to use in clinical practice for examining blood cell microscopy. In this research, the images were classified into five classes and the mean accuracy of the model for the five classes was 87%, which is considered a good accuracy for classification into five classes.

Keywords

• Image Processing
• Deep Learning
• Metaheuristic Algorithm
• Convolutional Neural Network
• Histopathology image processing

Main Subjects

• Artificial Intelligence, Robotics, Cognitive Computing