Perbandingan Distance Metric pada Nearest Neighbour untuk Klasifikasi Sel Darah Putih

2019 ◽  
Vol 11 (1) ◽  
pp. 16-19
Author(s):  
Felix Indra Kurniadi ◽  
Vinnia Kemala Putri
Keyword(s):  
Feature Extraction ◽  
Human Body ◽  
Blood Cells ◽  
Euclidean Distance ◽  
Local Binary Pattern ◽  
White Blood Cells ◽  
Nearest Neighbour ◽  
Harmful Substance ◽  
Distance Metric ◽  
Minkowski Distance

White blood cells, have a function to protect human body from viruses, bacteria and any other harmful substance. In this research, Local Binary Pattern was proposed for feature extraction using Euclidean distance, Chebyshev distance and Minkowski distance as classifier.

Perbandingan Local Binary Pattern untuk Klasifikasi Sel Darah Putih

2018 ◽  
Vol 9 (2) ◽  
pp. 118-121
Author(s):  
Felix Indra Kurniadi
Keyword(s):  
Support Vector Machine ◽  
Blood Cells ◽  
Local Binary Pattern ◽  
White Blood Cells ◽  
Support Vector ◽  
Nearest Neighbour ◽  
Rotation Invariant ◽  
Blood Cancer ◽  
Index Terms ◽  
Uniform Local Binary Pattern

In recent year, a lot of researches try to overcome problem in recognition and classify white blood cells to help hematologists diagnose white blood cells disease such blood cancer, leukemia and AIDS. This paper compares several methods Local Binary Pattern such as Local Binary Pattern Uniform, Local Binary Pattern Rotation Invariant and Local Binary Pattern Rotation Invariant Uniform to classify five types of white blood cells using two classifier: Support Vector Machine and K-Nearest Neighbour. Index Terms—LBP, LBP-U, LBP-RI, LBP-RIU, white blood cells

scholarly journals Pemanfaatan Scale Invariant Feature Transform Berbasis Saliency untuk Klasifikasi Sel Darah Putih

2021 ◽  
Vol 7 (2) ◽  
Author(s):  
Yohannes Yohannes ◽  
Siska Devella ◽  
William Hadisaputra
Keyword(s):  
Feature Extraction ◽  
Blood Cell ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Scale Invariant Feature Transform ◽  
Scale Invariant ◽  
Invariant Feature ◽  
Feature Transform ◽  
Scale Invariant Feature

White blood cells are cells that makeup blood components that function to fight various diseases from the body (immune system). White blood cells are divided into five types, namely basophils, eosinophils, neutrophils, lymphocytes, and monocytes. Detection of white blood cell types is done in a laboratory which requires more effort and time. One solution that can be done is to use machine learning such as Support Vector Machine (SVM) with Scale Invariant Feature Transform (SIFT) feature extraction. This study uses a dataset of white blood cell images that previously carried out a pre-processing stage consisting of cropping, resizing, and saliency. The saliency method can take a significant part in image data and. The SIFT feature extraction method can provide the location of the keypoint points that SVM can use in studying and recognizing white blood cell objects. The use of region-contrast saliency with kernel radial basis function (RBF) yields the best accuracy, precision, and recall results. Based on the test results obtained in this study, saliency can improve the accuracy, precision, and recall of SVM on the white blood cell image dataset compared to without saliency.

Application of Genetic Algorithm and K-Nearest Neighbour Method in Real World Medical Fraud Detection Problem

2000 ◽  
Vol 4 (2) ◽  
pp. 130-137 ◽  
Author(s):  
Hongxing He ◽  
◽  
Simon Hawkins ◽  
Warwick Graco ◽  
Xin Yao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Decision Rule ◽  
Real World ◽  
Euclidean Distance ◽  
Fraud Detection ◽  
Classification Performance ◽  
Alternative Methods ◽  
Nearest Neighbour ◽  
Distance Metric ◽  
Nearest Neighbours

In the k-Nearest Neighbour (kNN) algorithm, the classification of a new sample is determined by the class of its k nearest neighbours. The performance of the kNN algorithm is influenced by three main factors: (1) the distance metric used to locate the nearest neighbours; (2) the decision rule used to derive a classification from the k-nearest neighbours; and (3) the number of neighbours used to classify the new sample. Using k = 1, 3, or 5 nearest neighbours, this study uses a Genetic Algorithm (GA) to find the optimal non-Euclidean distance metric in the kNN algorithm and examines two alternative methods (Majority Rule and Bayes Rule) to derive a classification from the k nearest neighbours. This modified algorithm was evaluated on two real-world medical fraud problems. The General Practitioner (GP) database is a 2-class problem in which GPs are classified as either practising appropriately or inappropriately. The ’.Doctor-Shoppers’ database is a 5-class problem in which patients are classified according to the likelihood that they are ’doctor-shoppers’. Doctor-shoppers are patients who consult many physicians in order to obtain multiple prescriptions of drugs of addiction in excess of their own therapeutic need. In both applications, classification accuracy was improved by optimising the distance metric in the kNN algorithm. The agreement rate on the GP dataset improved from around 70% (using Euclidean distance) to 78 % (using an optimised distance metric), and from about 55% to 82% on the Doctor Shopper’s dataset. Differences in either the decision rule or the number of nearest neighbours had little or no impact on the classification performance of the kNN algorithm. The excellent performance of the kNN algorithm when the distance metric is optimised using a genetic algorithm paves the way for its application in the real world fraud detection problems faced by the Health Insurance Commission (HIC).

scholarly journals COMPREHENSIVE STUDY ON ASTHISANKHYA SHAREERA IN RELATION TO ANATOMY

2020 ◽  
Vol 8 (10) ◽  
pp. 4801-4805
Author(s):  
Geethu Arumughan ◽  
Swapna Kumary
Keyword(s):  
Calcium Phosphate ◽  
Connective Tissue ◽  
Human Body ◽  
Blood Cells ◽  
White Blood Cells ◽  
Organic Matrix ◽  
Modern Human ◽  
The Body ◽  
Human Anatomy ◽  
Comprehensive Study

Acharya Sushruta explained that the organs of the body destroy after death except Asthi. Importance of Asthi & Sandhi is explained that it supports the human body just as trees are supported by the middle core Sara present inside the trunk. Knowledge of Asthi can be traced back from the Vedas passing chronologi-cally down to Samhitas. Bones are the hard-connective tissue forming the substance of the skeleton of most vertebrates, composed of a collagen rich organic matrix impregnated with calcium, phosphate, and other minerals. Bones protect the various organs of the body, produce red and white blood cells, store minerals, provide structure and support for the body and enable mobility. Importance of knowledge of Shareera Sankhya is given in our Samhitas. It explained that the clinician who has knowledge about hu-man body with its all parts with their enumeration never gets distracted. There is different opinion regarding the number of Asthi in different Samhitas. According to Acharya Charaka and Vagbhata 360 bones are present in Shareera. 300 bones are present in the Shareera according to Sushruta. The adult human body has 206 bones as per modern anatomy. Here is an attempt to compare the Acharyas’s view of Asthi San-khya Shareera with the modern human anatomy.

Automatic localization and feature extraction of white blood cells

1995 ◽  
Author(s):  
Vassili A. Kovalev ◽  
Andrei Y. Grigoriev ◽  
Hyo-Sok Ahn ◽  
Nickolai K. Myshkin
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
White Blood Cells ◽  
Automatic Localization

An integrated approach for different attribute types in nearest neighbour classification

1996 ◽  
Vol 11 (3) ◽  
pp. 245-252
Author(s):  
W. Z. Liu
Keyword(s):  
Machine Learning ◽  
Euclidean Distance ◽  
Integrated Approach ◽  
Nearest Neighbour ◽  
Distance Metric ◽  
Classification Techniques ◽  
Feature Spaces ◽  
Classification Tasks ◽  
Combined Work ◽  
Mixed Types

AbstractThe basic nearest neighbour algorithm works by storing the training instances and classifying a new case by predicting that it has the same class as its nearest stored instance. To measure the distance between instances, some distance metric needs to be used. In situations when all attributes have numeric values, the conventional nearest neighbour method treats examples as points in feature spaces and uses Euclidean distance as the distance metric. In tasks with only nominal attributes, the simple “over-lap” metric is usually used. To handle classification tasks that have mixed types of attributes, the two different metrics are simply combined. Work by researchers in the machine learning field has shown that this approach performs poorly. This paper attempts to study a more recently developed distance metric and show that this metric is capable of measuring the importance of different attributes. With the use of discretisation for numeric-valued attributes, this method provides an integrated way in dealing with problem domains with mixtures of attribute types. Through detailed analyses, this paper tries to provide further insights into the understanding of nearest neighbour classification techniques and promote further use of this type of classification algorithm.

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

2021 ◽  
Vol 6 (1) ◽  
pp. 63
Author(s):  
Anwar Siswanto Musliman ◽  
Abdul Fadlil ◽  
Anton Yudhana
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
White Blood Cells ◽  
Training Data ◽  
Classification Model ◽  
Automatic Identification ◽  
K Nearest Neighbor ◽  
Machine Learning Classification ◽  
Cell Image

In various disease diagnoses, one of the parameters is white blood cells, consisting of eosinophils, basophils, neutrophils, lymphocytes, and monocytes. Manual identification takes a long time and tends to be subjective depending on the staff's experience, so the automatic identification of white blood cells will be faster and more accurate. White blood cells are identified by examining a colored blood smear (SADT) and examined under a digital microscope to obtain a cell image. Image identification of white blood cells is determined through HSV color space segmentation (Hue, Saturation Value) and feature extraction of the Gray Level Cooccurrence Matrix (GLCM) method using the Angular Second Moment (ASM), Contrast, Entropy, and Inverse Different Moment (IDM) features. The purpose of this study was to identify white blood cells by comparing the classification accuracy of the K-nearest neighbor (KNN), Naïve Bayes Classification (NBC), and Multilayer Perceptron (MLP) methods. The classification results of 100 training data and 50 white blood cell image testing data. Tests on the KNN, NBC, and MLP methods yielded an accuracy of 82%, 80%, and 94%, respectively. Therefore, MLP was chosen as the best classification model in the identification of white blood cells.

scholarly journals White Blood Cells Detection using YOLOv3 with CNN Feature Extraction Models

2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Nurasyeera Rohaziat ◽  
Mohd Razali ◽  
Wan Nurshazwani ◽  
Nurmiza Othman
Keyword(s):  
Feature Extraction ◽  
Blood Cells ◽  
White Blood Cells
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