Analisis Sel Darah Putih dengan Pendekatan Bioinformatika menggunakan Arsitektur MobileNetV2 - Dimas Febri Kuncoro; Rian Ardianto (Universitas Harapan Bangsa, 2024)
The identification and classification of white blood cells have become important in medical image analysis for disease diagnosis and health monitoring. Traditional classification methods often consume time and are less reliable. Based on these issues, this study aims to implement a Convolutional Neural Network (CNN) with the MobileNetV2 architecture in the classification of white blood cells to improve efficiency and accuracy. This research method emphasizes the use of the MobileNetV2 architecture in CNN. The training process is conducted using Google Colaboratory with the aid of TensorFlow. Model evaluation is carried out using various standard metrics, including accuracy, precision, recall, and F1-score. The results of the study show that the implementation of CNN with the MobileNetV2 architecture produces an efficient and accurate white blood cell classification model. Through a training process with 15 epochs, the model achieved a high accuracy rate and a low error rate. The accuracy rate in this study indicated an accuracy result of 94.8%. The model evaluation demonstrated good performance in classifying different types of white blood cells, as shown by the evaluation metrics and the confusion matrix. This model implementation has great potential to be used in medical image analysis for efficient and accurate disease diagnosis and health monitoring. Implementasi model ini memiliki potensi besar untuk digunakan dalam analisis citra medis untuk diagnosis penyakit dan pemantauan kesehatan yang efisien dan akurat.