Outcome prediction in endoscopic surgery for chronic rhinosinusitis – A multidimensional model

Szaleniec, Joanna; Szaleniec, Maciej; Stręk, Paweł; Boroń, Aleksandra; Jabłońska, Katarzyna; Gawlik, Jolanta; Składzień, Jacek

doi:10.1016/j.advms.2013.06.003

Cited by 8 publications

(8 citation statements)

References 29 publications

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“…Endoscopic sinus surgery (ESS) has become the treatment of choice for refractory inflammatory diseases of the paranasal sinuses . In the United States (US), an estimated 250,000 cases of ESS are performed each year .…”

mentioning

confidence: 99%

Effect of diabetes mellitus on postoperative endoscopic sinus surgery outcomes

Filimonov

Chung

Wong

et al. 2017

Int Forum Allergy Rhinol

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mentioning

confidence: 99%

Effect of diabetes mellitus on postoperative endoscopic sinus surgery outcomes

Filimonov

Chung

Wong

et al. 2017

Int Forum Allergy Rhinol

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“…The detection pipeline is a general framework and there are no strict restrictions. Hence, one could apply the same process to, for example, CT scan images [ 64 ], and even other areas such as those discussed in [ 65 ].…”

Section: Discussionmentioning

confidence: 99%

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Karbhari¹,

Basu

Geem

et al. 2021

Diagnostics

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COVID-19 is a disease caused by the SARS-CoV-2 virus. The COVID-19 virus spreads when a person comes into contact with an affected individual. This is mainly through drops of saliva or nasal discharge. Most of the affected people have mild symptoms while some people develop acute respiratory distress syndrome (ARDS), which damages organs like the lungs and heart. Chest X-rays (CXRs) have been widely used to identify abnormalities that help in detecting the COVID-19 virus. They have also been used as an initial screening procedure for individuals highly suspected of being infected. However, the availability of radiographic CXRs is still scarce. This can limit the performance of deep learning (DL) based approaches for COVID-19 detection. To overcome these limitations, in this work, we developed an Auxiliary Classifier Generative Adversarial Network (ACGAN), to generate CXRs. Each generated X-ray belongs to one of the two classes COVID-19 positive or normal. To ensure the goodness of the synthetic images, we performed some experimentation on the obtained images using the latest Convolutional Neural Networks (CNNs) to detect COVID-19 in the CXRs. We fine-tuned the models and achieved more than 98% accuracy. After that, we also performed feature selection using the Harmony Search (HS) algorithm, which reduces the number of features while retaining classification accuracy. We further release a GAN-generated dataset consisting of 500 COVID-19 radiographic images.

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“…This process requires skill and attention and is time consuming [ 13 ]. Computer-aided diagnostics (CAD) systems bypass these problems, consume little time, and provide much better accuracy than these manual techniques [ 14 , 15 ]. CAD systems’ history is not very impressive because they work in several interlocked steps, including preprocessing, handcrafted feature extraction, and then classification, which tend to constrain the overall classification accuracy (OA).…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

et al. 2021

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Manual diagnosis of skin cancer is time-consuming and expensive; therefore, it is essential to develop automated diagnostics methods with the ability to classify multiclass skin lesions with greater accuracy. We propose a fully automated approach for multiclass skin lesion segmentation and classification by using the most discriminant deep features. First, the input images are initially enhanced using local color-controlled histogram intensity values (LCcHIV). Next, saliency is estimated using a novel Deep Saliency Segmentation method, which uses a custom convolutional neural network (CNN) of ten layers. The generated heat map is converted into a binary image using a thresholding function. Next, the segmented color lesion images are used for feature extraction by a deep pre-trained CNN model. To avoid the curse of dimensionality, we implement an improved moth flame optimization (IMFO) algorithm to select the most discriminant features. The resultant features are fused using a multiset maximum correlation analysis (MMCA) and classified using the Kernel Extreme Learning Machine (KELM) classifier. The segmentation performance of the proposed methodology is analyzed on ISBI 2016, ISBI 2017, ISIC 2018, and PH2 datasets, achieving an accuracy of 95.38%, 95.79%, 92.69%, and 98.70%, respectively. The classification performance is evaluated on the HAM10000 dataset and achieved an accuracy of 90.67%. To prove the effectiveness of the proposed methods, we present a comparison with the state-of-the-art techniques.

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Outcome prediction in endoscopic surgery for chronic rhinosinusitis – A multidimensional model

Cited by 8 publications

References 29 publications

Effect of diabetes mellitus on postoperative endoscopic sinus surgery outcomes

Effect of diabetes mellitus on postoperative endoscopic sinus surgery outcomes

Generation of Synthetic Chest X-ray Images and Detection of COVID-19: A Deep Learning Based Approach

Skin Lesion Segmentation and Multiclass Classification Using Deep Learning Features and Improved Moth Flame Optimization

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