scholarly journals KNN Dan Gabor Filter Serta Wiener Filter Untuk Mendiagnosis Penyakit Pneumonia Citra X-RAY Pada Paru-Paru

2021 ◽  
Vol 1 (2) ◽  
pp. 147-155
Author(s):  
Felix Antony ◽  
Hafiz Irsyad ◽  
Muhammad Ezar Al Rivan
Keyword(s):  
Image Enhancement ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Wiener Filter ◽  
K Nearest Neighbor ◽  
X Ray

Pneumonia adalah salah satu jenis penyakit paru-paru yang disebabkan oleh bakteri, virus, jamur, ataupun parasit. Salah satu cara untuk mengetahui penyakit pneumonia adalah dengan rontgen atau x-ray. Hasil rontgen akan dianalisis untuk mengetahui apakah terdapat pneumonia atau tidak. Penelitian ini bertujuan untuk mengklasifikasi hasil rontgen apakah terdapat pneumonia atau tidak pada hasil rontgen. metode yang digunakan untuk klasifikasi adalah K-Nearest Neighbor (KNN) dan metode ekstraksi Gabor Filter serta Wiener Filter. Tahapan yang dilaukan pada citra sebelum di Klasifikasi yaitu Resize, selanjutnya dilakukan ekstraksi menggunakan Gabor Filter, Image Enhancement menggunakan Wiener Filter dan di klasifikasi menggunakan K-Nearest Neighbor (KNN) menghasilkan akurasi terbaik sebesar 79,62%.

scholarly journals Analysis of gabor filter based features with PCA and GA for the detection of drusen in fundus images

2018 ◽  
Vol 7 (1) ◽  
pp. 115
Author(s):  
Sheela N. ◽  
Basavaraj L.
Keyword(s):  
Genetic Algorithm ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Age Related Macular Degeneration ◽  
Misclassification Rate ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Automated Method ◽  
Age Related ◽  
Predictive Rate

Human eye can be affected by different types of diseases. Age-Related Macular Degeneration (AMD) is one of the such diseases, and it mainly occurs after 50 years of age. This disease is characterized by the occurrence of yellow spots called as Drusen. In this work, an automated method for the detection of drusen in Fundus image has been developed, and it has been tested on 70 images consisting of 30 normal images and 40 images with drusen. Performance of the Support Vector Machine (SVM) and K Nearest Neighbor (KNN) classifier has been evaluated using Data's reduction using Principle Component Analysis (PCA) and Data's selection using Genetic Algorithm (GA).Performance evaluation has been done in terms of accuracy, sensitivity, specificity, misclassification rate, positive predictive rate, negative predictive rate and Youden’s Index. The proposed method has achieved highest accuracy of 98.7% when data selection using Genetic Algorithm has been applied.

scholarly journals Automated Systems for Detection of COVID-19 Using Chest X-ray Images and Lightweight Convolutional Neural Networks

2020 ◽  
Author(s):  
Ali Mohammad Alqudah ◽  
Shoroq Qazan ◽  
Amin Alqudah
Keyword(s):  
Early Diagnosis ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Softmax Classifier ◽  
Chest X Ray ◽  
Novel Coronavirus ◽  
Sensitivity Specificity

Abstract Since December 2019, the appearance of an outbreak of a novel coronavirus disease namely COVID-19 and which is previously known as 2019-nCoV. COVID-19 is a type of coronavirus that leads to the general destruction of respiratory systems and a severe respiratory symptom which are associated with highly Intensive Care Unit (ICU) admissions and death. Like any disease, the early diagnosis of coronavirus leads to limit its wide-spreading and increases the recovery rates of patients. The gold standard of COVID-19 detection is the real-time reverse transcription-polymerase chain reaction (RT-PCR) which has been used by the clinician to discover the presence or absence of this type of virus. The clinicians report that this technique has a low positive rate in the early stage of this disease. Based on this, the clinicians were forced to use another way to help in the early diagnosis of COVID-2019. So, the clinician's attention moved towards the medical imaging modalities especially the computed Tomography (CT) and X-ray chest images. Both modalities show that there is a change in the lungs in the case of COVID-19 that is different from any other type of pneumonic disease. Therefore, this research targeted toward employing different Artificial Intelligence (AI) techniques to propose a system for early detection of COVID-19 using chest X-ray images. These images are classified using different AI algorithms and a combination of them, then their performance was evaluated to recognize the best of them. These algorithms include a convolutional neural network (CNN), Softmax, support vector machine (SVM), Random Forest, and K nearest neighbor (KNN). Here CNN is into two scenarios, the first one to classify the X-ray images using a softmax classifier, and the second one to extract automated features from the images and pass these features to other classifiers (SVM, RFF, and KNN). According to the results, the performance of all classifiers is good and most of them record accuracy, sensitivity, specificity, and precision of more than 98%.

scholarly journals Analisis Perbandingan Algoritma Klasifikasi Citra Chest X-ray Untuk Deteksi Covid-19

Teknika ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 96-103
Author(s):  
Mohammad Farid Naufal ◽  
Selvia Ferdiana Kusuma ◽  
Kevin Christian Tanus ◽  
Raynaldy Valentino Sukiwun ◽  
Joseph Kristiano ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
K Nearest Neighbors ◽  
X Ray ◽  
Chest X Ray

Kondisi pandemi global Covid-19 yang muncul diakhir tahun 2019 telah menjadi permasalahan utama seluruh negara di dunia. Covid-19 merupakan virus yang menyerang organ paru-paru dan dapat mengakibatkan kematian. Pasien Covid-19 banyak yang telah dirawat di rumah sakit sehingga terdapat data citra chest X-ray paru-paru pasien yang terjangkit Covid-19. Saat ini sudah banyak peneltian yang melakukan klasifikasi citra chest X-ray menggunakan Convolutional Neural Network (CNN) untuk membedakan paru-paru sehat, terinfeksi covid-19, dan penyakit paru-paru lainnya, namun belum ada penelitian yang mencoba membandingkan performa algoritma CNN dan machine learning klasik seperti Support Vector Machine (SVM), dan K-Nearest Neighbor (KNN) untuk mengetahui gap performa dan waktu eksekusi yang dibutuhkan. Penelitian ini bertujuan untuk membandingkan performa dan waktu eksekusi algoritma klasifikasi K-Nearest Neighbors (KNN), Support Vector Machine (SVM), dan CNN  untuk mendeteksi Covid-19 berdasarkan citra chest X-Ray. Berdasarkan hasil pengujian menggunakan 5 Cross Validation, CNN merupakan algoritma yang memiliki rata-rata performa terbaik yaitu akurasi 0,9591, precision 0,9592, recall 0,9591, dan F1 Score 0,959 dengan waktu eksekusi rata-rata sebesar 3102,562 detik.

scholarly journals Classification of lung condition for early diagnosis of pneumonia and tuberculosis based on embedded system

2021 ◽  
Vol 10 (3) ◽  
pp. 1262-1270
Author(s):  
Rizal Maulana ◽  
Alfatehan Arsya Baharin ◽  
Hurriyatul Fitriyah
Keyword(s):  
Carbon Dioxide ◽  
Embedded System ◽  
Nearest Neighbor ◽  
Image Data ◽  
Early Research ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Lung Condition ◽  
Chest X Ray

The lungs are the main organs in the respiratory system that have a function as a place for exchange of oxygen and carbon dioxide. Due to the importance of lung function, indications of lung disorders must be detected and diagnosed early. Research on the classification of lung conditions generally uses chest x-ray image data. Where a time-consuming procedure is needed to obtain the data. In this research, an embedded system to diagnose lung conditions was designed. The system was made to be easy to use independently and provides real-time examination results. This system uses parameters of body temperature, oxygen saturation, fingernail color and lung volume in classifying lung conditions. There are three conditions that can be classified by the system, that is healthy lungs, pneumonia and tuberculosis. The k-nearest neighbor method was used in the classification process in the designed system. The dataset used was 51 data obtained from the hospital. Each data already has a label in the form of lung condition based on the doctor’s diagnosis. The proposed system has an accuracy of 88.24% in classifying lung conditions.

scholarly journals KLASIFIKASI PNEUMONIA MENGGUNAKAN METODE K-NEAREST NEIGHBOR DENGAN EKSTRAKSI GLCM

2020 ◽  
Vol 1 (1) ◽  
pp. 33-44
Author(s):  
Chandra Wijaya ◽  
Hafiz Irsyad ◽  
Wijang Widhiarso
Keyword(s):  
Extraction Method ◽  
Nearest Neighbor ◽  
Good Accuracy ◽  
Classification Method ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Contrast Stretching ◽  
Parenchymal Disease

Pneumonia is an inflammatory parenchymal disease caused by various microorganisms, including bacteria, micro bacteria, fungi, and viruses. This study used an X-ray to find out whether or not there was pneumonia. The objective of this study was to classify the X-ray results whether or not there was pneumonia in a fast and precise way through a program to produce good accuracy. The classification method used in this study were K-Nearest Neighbor (KNN) and Gray Level Co-Occurrence (GLCM) for the extraction method. There are several stages before being classified, namely cropping, resizing, contrast stretching, and thresholding. The results showed that the best accuracy per class was 66.20% for K = 5.

Tuberculosis detection in chest X-ray using Mayfly-algorithm optimized dual-deep-learning features

2021 ◽  
pp. 1-14
Author(s):  
M.P. Rajakumar ◽  
R. Sonia ◽  
B. Uma Maheswari ◽  
S.P. Karuppiah
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Binary Classification ◽  
Primary Objective ◽  
World Health ◽  
Experimental Investigations ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Health Organization

World-Health-Organization (WHO) has listed Tuberculosis (TB) as one among the top 10 reasons for death and an early diagnosis will help to cure the patient by giving suitable treatment. TB usually affects the lungs and an accurate bio-imaging scheme will be apt to diagnose the infection. This research aims to implement an automated scheme to detect TB infection in chest radiographs (X-ray) using a chosen Deep-Learning (DL) approach. The primary objective of the proposed scheme is to attain better classification accuracy while detecting TB in X-ray images. The proposed scheme consists of the following phases namely, (1) image collection and pre-processing, (2) feature extraction with pre-trained VGG16 and VGG19, (3) Mayfly-algorithm (MA) based optimal feature selection, (4) serial feature concatenation and (5) binary classification with a 5-fold cross validation. In this work, the performance of the proposed DL scheme is separately validated for (1) VGG16 with conventional features, (2) VGG19 with conventional features, (3) VGG16 with optimal features, (4) VGG19 with optimal features and (5) concatenated dual-deep-features (DDF). All experimental investigations are conducted and achieved using MATLAB® program. Experimental outcome confirms that the proposed system with DDF yields a classification accuracy of 97.8%using a K Nearest-Neighbor (KNN) classifier.

scholarly journals A hybrid deep learning approach towards building an intelligent system for pneumonia detection in chest X-ray images

2021 ◽  
Vol 11 (6) ◽  
pp. 5530
Author(s):  
Ihssan S. Masad ◽  
Amin Alqudah ◽  
Ali Mohammad Alqudah ◽  
Sami Almashaqbeh
Keyword(s):  
Deep Learning ◽  
Hybrid System ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Detection System ◽  
Learning System ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
X Ray ◽  
Chest X Ray

<span>Pneumonia is a major cause for the death of children. In order to overcome the subjectivity and time consumption of the traditional detection of pneumonia from chest X-ray images; this work hypothesized that a hybrid deep learning system that consists of a convolutional neural network (CNN) model with another type of classifiers will improve the performance of the detection system. Three types of classifiers (support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF) were used along with the traditional CNN classification system (Softmax) to automatically detect pneumonia from chest X-ray images. The performance of the hybrid systems was comparable to that of the traditional CNN model with Softmax in terms of accuracy, precision, and specificity; except for the RF hybrid system which had less performance than the others. On the other hand, KNN hybrid system had the best consumption time, followed by the SVM, Softmax, and lastly the RF system. However, this improvement in consumption time (up to 4 folds) was in the expense of the sensitivity. A new hybrid artificial intelligence methodology for pneumonia detection has been implemented using small-sized chest X-ray images. The novel system achieved a very efficient performance with a short classification consumption time.</span>

scholarly journals Human Retina Based Identification System Using Gabor Filters and GDA Technique

2020 ◽  
Vol 16 (3) ◽  
pp. 243-253
Author(s):  
Shahad Sultan ◽  
Mayada Faris Ghanim
Keyword(s):  
Feature Extraction ◽  
Discriminant Analysis ◽  
Nearest Neighbor ◽  
Gabor Filter ◽  
Identification System ◽  
Human Retina ◽  
K Nearest Neighbor ◽  
Characteristic Features ◽  
The Individual ◽  
Generalized Discriminant Analysis

A biometric authentication system provides an automatic person authentication based on some characteristic features possessed by the individual. Among all other biometrics, human retina is a secure and reliable source of person recognition as it is unique, universal, lies at the back of the eyeball and hence it is unforgeable. The process of authentication mainly includes pre-processing, feature extraction and then features matching and classification. Also authentication systems are mainly appointed in verification and identification mode according to the specific application. In this paper, preprocessing and image enhancement stages involve several steps to highlight interesting features in retinal images. The feature extraction stage is accomplished using a bank of Gabor filter with number of orientations and scales. Generalized Discriminant Analysis (GDA) technique has been used to reduce the size of feature vectors and enhance the performance of proposed algorithm. Finally, classification is accomplished using k-nearest neighbor (KNN) classifier to determine the identity of the genuine user or reject the forged one as the proposed method operates in identification mode. The main contribution in this paper is using Generalized Discriminant Analysis (GDA) technique to address ‘curse of dimensionality’ problem. GDA is a novel method used in the area of retina recognition.

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