Klasifikasi Sel Darah Putih dan Sel Limfoblas Menggunakan Metode Multilayer Perceptron Backpropagation

Abstract

Leukemia is a type of cancer that is on white blood cell. This disease are characterized by abundance of abnormal white blood cell called lymphoblast in the bone marrow. Classification of blood cell types, calculation of the ratio of cell types and comparison with normal blood cells can be the subject of diagnosing this disease. The diagnostic process is carried out manually by hematologists through microscopic image. This method is likely to provide a subjective result and time-consuming.The application of digital image processing techniques and machine learning in the process of classifying white blood cells can provide more objective results. This research used thresholding method as segmentation and multilayer method of back propagation perceptron with variations in the extraction of textural features, geometry, and colors. The results of segmentation testing in this study amounted to 68.70%. Whereas the classification test shows that the combination of feature extraction of GLCM features, geometry features, and color features gives the best results. This test produces an accuration value 91.43%, precision value of 50.63%, sensitivity 56.67%, F1Score 51.95%, and specitifity 94.16%.

Optimasi Hyperparameter Multilayer Perceptron Untuk Prediksi Daya Beli Mobil

“…Prediksi tersebut kemudian dievaluasi dan dibandingkan untuk menemukan prediksi yang memberikan kinerja terbaik dengan model multilayer perceptron. Multilayer perceptron sering diterapkan untuk meneliti data yang kompleks karena mampu mengnalisa data dengan baik [9]. Pada sistem kerjanya dalam menyelesaikan masalah multilayer perceptron meniru daya syaraf otak manusia dalam bentuk neuron-neuron [10].…”

Vehicle Monitoring with Web-Based Applications Utilizing High-Precision Positioning

“…Backpropagation is a machine learning algorithm with a backpassing ability to correct errors in the classification process using its three layers: the input layers, the hidden layers, and the output layers [13]. Backpropagation is a widely utilized technique in classification research, as demonstrated in the study about signature image identification with 83% accuracy [14], the white blood and lymphoblast cells classification with 91.43% accuracy [15], and the eyeball movement patterns classification with 88.24% accuracy [16]. Backpropagation algorithms that use three or more hidden layers are known as Multilayer Backpropagation, and neural networks, such as the Multilayer Perceptron, use it in their architecture [17].…”

Identification of White Blood Cells Using Machine Learning Classification Based on Feature Extraction

“…Liyantoko et al Researching the classification of white blood cells and lymphoblasts using the Multilayer Perception (MLP) and backpropagation method [10] on GLCM feature extraction of geometric and color features resulted in an accuracy value of 91.43% and a precision of 50.63%.…”