Accurate and efficient shape matching approach using vocabularies of multi-feature space representations

Khalid, Shehzad; Sajjad, Sadaf; Jabbar, Sohail; Chang, Hangbae

doi:10.1007/s11554-015-0545-z

Cited by 14 publications

(3 citation statements)

References 36 publications

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“…Voting ensemble-based method for making a final forecast is also being studied to reduce categorization error. When a majority of votes are cast in favor of a particular method reduced [ 35 , 36 ]. Finally, we used this ensemble-based method to improve the efficacy of these methods.…”

Section: Proposed Methodologymentioning

confidence: 99%

Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model

et al. 2022

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Population size has made disease monitoring a major concern in the healthcare system, due to which auto-detection has become a top priority. Intelligent disease detection frameworks enable doctors to recognize illnesses, provide stable and accurate results, and lower mortality rates. An acute and severe disease known as Coronavirus (COVID19) has suddenly become a global health crisis. The fastest way to avoid the spreading of Covid19 is to implement an automated detection approach. In this study, an explainable COVID19 detection in CT scan and chest X-ray is established using a combination of deep learning and machine learning classification algorithms. A Convolutional Neural Network (CNN) collects deep features from collected images, and these features are then fed into a machine learning ensemble for COVID19 assessment. To identify COVID19 disease from images, an ensemble model is developed which includes, Gaussian Naive Bayes (GNB), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbor (KNN), and Random Forest (RF). The overall performance of the proposed method is interpreted using Gradient-weighted Class Activation Mapping (Grad-CAM), and t-distributed Stochastic Neighbor Embedding (t-SNE). The proposed method is evaluated using two datasets containing 1,646 and 2,481 CT scan images gathered from COVID19 patients, respectively. Various performance comparisons with state-of-the-art approaches were also shown. The proposed approach beats existing models, with scores of 98.5% accuracy, 99% precision, and 99% recall, respectively. Further, the t-SNE and explainable Artificial Intelligence (AI) experiments are conducted to validate the proposed approach.

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Section: Proposed Methodologymentioning

confidence: 99%

Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model

et al. 2022

Self Cite

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“…The third paper, entitled ''Accurate and efficient shape matching approach using vocabularies of multi-feature space representations,'' by Khalid et al [10] proposed an approach for efficient shape retrieval using a combination of Fourier descriptors and inner-distance-based shape context features. They preprocessed the shapes to account for the variation in scale and the presence of cracks, and generated multiple feature space representations of shapes using a hierarchical indexing structure to retrieve shapes both efficiently and accurately.…”

Section: Real-time Perceptual-inspired Imaging Systemsmentioning

confidence: 99%

GUEST EDITORIAL special issue on real-time perceptual-inspired imaging systems with computational science and aesthetics

Seo

Chen

Rho

2017

J Real-Time Image Proc

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“…Daha sonrasında, elde edilen öznitelik vektörü de sınıflandırma işleminde kullanılmaktadır. Aynı işlem farklı konulardaki birçok uygulama içinde gerçekleştirilmektedir [4].…”

Section: Introductionunclassified

Derin Öğrenme ve Destek Vektör Makineleri İle Görüntüden Cinsiyet Tahmini

Metlek

Kayaalp

2020

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Yapılan çalışmada günümüzün popüler konularından olan derin öğrenme algoritmaları üzerine bir uygulama geliştirilmiştir. Geliştirilen uygulamada görüntülerden yüz tespiti yapılıp sonrasında görüntüdeki kişinin cinsiyet tahmini gerçekleştirilmiştir. Bu uygulamada Wiki görüntü veri tabanından elde edilen 62328 görüntü kullanılmıştır. Kullanılan görüntüler üzerinde, yüz görüntüsü bulunmayanlar veri setinden çıkartılarak yeni bir veri seti oluşturulmuştur. Oluşturulan veri setindeki görüntülerden, ileri derin öğrenme tekniklerinden biri olan Evrişimsel Sinir Ağları (ESA) yöntemi kullanılarak öznitelikler çıkartılmıştır. Elde edilen öznitelikler Destek Vektör Makinesi (DVM) kullanılarak sınıflandırılmıştır. Sınıflandırma başarısı karmaşıklık matrisi ile gösterilmiş olup, %94,48 başarı oranı ile sınıflandırma işlemi gerçekleştirilmiştir.

show abstract

Accurate and efficient shape matching approach using vocabularies of multi-feature space representations

Cited by 14 publications

References 36 publications

Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model

Explainable artificial intelligence approach in combating real-time surveillance of COVID19 pandemic from CT scan and X-ray images using ensemble model

GUEST EDITORIAL special issue on real-time perceptual-inspired imaging systems with computational science and aesthetics

Derin Öğrenme ve Destek Vektör Makineleri İle Görüntüden Cinsiyet Tahmini

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