Multi‐features fusion classification method for texture image

Jing, Chen; Zhang, Yanxin; Jiang, Yuanyuan

doi:10.1049/joe.2018.9118

Cited by 4 publications

(2 citation statements)

References 7 publications

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“…A series of experiments is the starting point for feature extraction, which then produces derived features that are meant to be valuable, making learning and adaptation easier and, in some circumstances, contribute to higher human interpretations. A reduced set of feature vectors could be generated from input data that is too large to analyze and process the dataset [24,25] . In the proposed scheme two different sorts of descriptors, namely HOG and LPQ feature extractors were utilized to derive features from the CXR images.…”

Section: Feature Extractionmentioning

confidence: 99%

“…A new feature matrix was created by concatenating the two feature matrices, which was then the fused feature matrix feed into the machine learning algorithm for training and performing prediction on testing. The proposed model was used to classify the data into two categories either COVID or Non-COVID, and the results were compared to a single texture feature classification strategy, which was found to be more accurate and better in all metrics [24] . The extracted feature vectors were fused by concatenation and depicted in Figure (6).…”

Section: Feature Fusionmentioning

confidence: 99%

See 1 more Smart Citation

Classification of COVID-19 on Chest X-Ray Images Through the Fusion of HOG and LPQ Feature Sets

Hamaamin¹,

Wady²,

Kareem³

2022

PJBAS

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Covid-19 is a contagious disease that affects people's everyday life, personal health, as well as a nation's economy. COVID-19 infected individuals, according to a clinical study, are most usually contaminated with a severe condition after coming into a primary infection. The chest radiograph (also known as the chest X-ray or CXR) or a chest CT scan is a more reliable imaging method for diagnosing COVID-19 infected individuals. This article proposed a novel technique for classifying CXR scan images as healthy or affected COVID-19 by fusing the features extracted using Histogram of Oriented Gradient (HOG) and Local Phase Quantization (LPQ). This research is an experimental study that employed 7232 CXR images from a COVID-19 Radiography dataset as training and testing data. As a result, by using both individual and fused feature extraction methodologies, a developed model was created and fed into the machine learning techniques. The testing results reveal that the improved architecture outperforms current methods for identifying COVID-19 patients in terms of accuracy rate, which reached 97.15 %.

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Section: Feature Extractionmentioning

confidence: 99%

Section: Feature Fusionmentioning

confidence: 99%