Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)

Self Cite

One of the diagnosis procedures for acute lymphoblastic leukemia is screening for blood cells by expert operator using microscope. This process is relatively long and will slow healing process of this disease which need fast treatment. Another way to screen this disease is by using digital image processing technique in microscopic image of blood smears to detect lymphoblast cells and types of white blood cells. One of essential step in digital image processing is segmentation because this process influences the subsequent process of detecting and classifying Acute Lymphoblastic Leukemia disease. This research performed segmentation of white blood cells using moving k-means algorithm. Some process are done to remove noise such as red blood cells and reduce detection errors such as white blood cells and/or lymphoblastic cell that’s appear overlap. Postprocessing are performed to improve segmentation quality and to separate connected white blood cell. The dataset in this study has been validated with expert clinical pathologists from Sardjito Regional General Hospital, Yogyakarta, Indonesia. This research produces systems performance with results in sensitivity of 85.6%, precision 82.3%, Fscore of 83,9% and accuracy of 72.3%. Based on the results of the testing process with a much larger number of datasets on the side of the variations level of cell segmentation difficulties both in terms of illumination and overlapping cell, the method proposed in this study was able to detect or segment overlapping white blood cells better.

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

Segmentation of White Blood Cells and Lymphoblast Cells Using Moving K-Means

Candradewi

Bagasjvara²

2018

Self Cite

“…The use of features as a segmentation and classification process that does not use CNN has also been used in several studies. There are several feature extraction methods commonly used for the object classification, such as K-Nearest Neighbor [11] and Gray-Level Cooccurrence Matrix. One of the most commonly used methods is Gray-Level Co-occurrence Matrix (GLCM).…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Classification Using Gray Level Co-occurrence Matrix and Convolutional Neural Network

Widhiarso¹,

Yohannes²,

Prakarsah³

2018

Image are objects that have many information. Gray Level Co-occurrence Matrix is one of many ways to extract information from image objects. Wherein, the extracted informations can be processed again using different methods, Gray Level Co-occurrence Matrix is use for clarifying brain tumor using Convolutional Neural Network. The scope in this research is to process the extracted information from Gray Level Co-occurrence Matrix to Convolutional Neural Network where it will processed as Deep Learning to measure the accuracy using four data combination from TI1, in the form of brain tumor data Meningioma, Glioma and Pituitary Tumor. Based on the implementation of this research, the classification result of Convolutional Neural Network shows that the contrast feature from Gray Level Co-occurrence Matrix can increase the accuracy level up to twenty percent than the other features. This extraction feature is also accelerate the classification process using Convolutional Neural Network.

“…Jaringan ini memiliki input berupa ciri hasil ekstraksi fitur dari citra sedangkan outputnya sebanyak enam buah sebagai klasifikasi enam buah sel darah putih dan sel limfoblas. Penentuan jumlah node hidden layer yang dihitung berdasarkan 2/3 jumlah node masukan ditambah jumlah node keluaran[10].Pada penelitian ini, dicari model terbaik dari kombinasi fitur GLCM. Kombinasi ini berdasarkan jumlah fitur pada GLCM yaitu kombinasi 2 fitur, 4 fitur, dan 6 fitur.…”

unclassified

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

Liyantoko

Candradewi

Harjoko

2019

Self Cite

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%.