Projective geometric model for automatic determination of X-ray-emitting source of a standard radiographic system

García-Ruesgas, Laura; Álvarez-Cuervo, Rafael; Valderrama-Gual, Francisco; Rojas‐Sola, José Ignacio

doi:10.1016/j.compbiomed.2018.06.016

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…At present condition, the covid is detected through a primary technique called Polymerase Chain Reaction (PCR) test. It is considered as more reliable option for health care practitioners [3,4]. Beyond these advantages, PCR is very time consuming process and sometime its kits are not available at all parts.…”

Section: Introductionmentioning

confidence: 99%

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Gopi¹,

Muthusamy²,

Suresh³

et al. 2022

Computers, Materials &Amp; Continua

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This research article proposes an automatic frame work for detecting COVID -19 at the early stage using chest X-ray image. It is an undeniable fact that coronovirus is a serious disease but the early detection of the virus present in human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in need of right and even rich technology for its early detection. The proposed deep learning model analysis the pixels of every image and adjudges the presence of virus. The classifier is designed in such a way so that, it automatically detects the virus present in lungs using chest image. This approach uses an image texture analysis technique called granulometric mathematical model. Selected features are heuristically processed for optimization using novel multi scaling deep learning called light weight residual-atrous spatial pyramid pooling (LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPP-Unet technique has a higher level of contracting solution by extracting major level of image features. Moreover, the corona virus has been detected using high resolution output. In the framework, atrous spatial pyramid pooling (ASPP) method is employed at its bottom level for incorporating the deep multi scale features in to the discriminative mode. The architectural working starts from the selecting the features from the image using granulometric mathematical model and the selected features are optimized using LightRES-ASPP-Unet. ASPP in the analysis of images has performed better than the existing Unet model. The proposed algorithm has achieved 99.6% of accuracy in detecting the virus at its early stage.

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Section: Introductionmentioning

confidence: 99%

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Gopi¹,

Muthusamy²,

Suresh³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…Due to the delays with the production and implementation of the vaccination campaign, prevention plays a major role and a controlled prognosis is only possible if the protocol therapy [2] is applied since the very early stages. The Polymerase Chain Reaction (PCR) test is the primary method for detecting COVID-19 as it is more reliable than any other option available to practitioners in the health & care system [3][4][5][6]. However, PCR is laborious, very time-consuming, and sometimes difficult to process due to shortages of kits and delays in their delivery in many countries worldwide.…”

Section: Introductionmentioning

confidence: 99%

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

2021

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This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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Projective geometric model for automatic determination of X-ray-emitting source of a standard radiographic system

Cited by 2 publications

References 34 publications

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

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