Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Pugalenthi, R.; Oliver, Avital; Anuradha, M

doi:10.1002/ima.22453

Cited by 10 publications

(6 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, two different features such as deep and LBP are extracted. The deep features are extracted from the chosen DLM, like GoogleNet, VGG16, VGG19, ResnEt18, ResNEt50 and ResNet101 [14,19,24,27,29,32]. After getting the necessary features, the LBP features with various weights presented in [7] are adopted, and the extracted features are then reduced to avoid the overfitting problem.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Therefore, computerized screening is always adopted to support the prescreening of the patient to generate the First Information Report (FIR) about the disease and its infection rate. The generated FIR is then examined by the doctors, and based on the report and doctor's observation, the treatment is planned and implemented to recover the patient to reduce the disease impact [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Kannan

Sivakumar

Ezhilarasi

2022

XST

View full text Add to dashboard Cite

Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm (FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases.

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Kannan

Sivakumar

Ezhilarasi

2022

XST

View full text Add to dashboard Cite

show abstract

“…The performance of noise filtering in MI was enhanced by utilizing NFN with the grouping of NF as well as post-processor (PP). Pugalenthi et al [112] has introduced the FA-based hybrid NF filter to enhance the performance of noise reduction respectively. The projected noise reduction merges the benefits of fuzzy, neural, as well as FA.…”

Section: Fa In Medical Image Pre-processingmentioning

confidence: 99%

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Nayak

Naik

Dinesh³

et al. 2020

SN COMPUT. SCI.

View full text Add to dashboard Cite

Since the past decades, most of the nature inspired optimization algorithms (NIOA) have been developed and become admired due to their effectiveness for resolving a variety of complex problems of dissimilar domain. Firefly algorithm (FA) is well-known, yet efficient nature inspired swarm intelligence (SI) based metaheuristic algorithm. Since from its initiation, FA has become well-liked between the researchers due to its competence and turn out to be an interesting technique for the practitioners as well as researchers for solving the problems of numerous fields of research such as classifications, clustering, neural networks, biomedical engineering, healthcare as well as other research domain. Moreover, there is an outstanding track record of FA in solving biomedical engineering (BME) and healthcare (HC) problems. Abundant complexities have been worked out with the assist of FA and its variants. By taking these particulars into concern, in this paper, a first ever in-depth analysis has been addressed on the variants, importance, applications as well as enhancements of FA in BME as well as HC. The major intention behind this investigative work is to motivate the researchers to improve and innovate new solutions for multifaceted problems of healthcare and biomedical engineering using FA.

show abstract

“…All of the above filters are efficient against impulse noise but fail due to blurring [11][12][13] at edges and loss of actual details in an image. Switching mechanisms [9,17], fuzzy based techniques [19][20][21][22][23][24][25][26][27][28], directional filters [15,16,22,24] and others [29][30][31][32][33][34][35][36][37][38][39] are good de-noising filters against random and universal noise but still lacking in detail preservation due to poor or no proper edge detection.…”

Section: Introductionmentioning

confidence: 99%

Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

et al. 2023

View full text Add to dashboard Cite

In this paper, a convolved feature vector based adaptive fuzzy filter is proposed for impulse noise removal. The proposed filter follows traditional approach, i.e., detection of noisy pixels based on certain criteria followed by filtering process. In the first step, proposed noise detection mechanism initially selects a small layer of input image pixels, convolves it with a set of weighted kernels to form a convolved feature vector layer. This layer of features is then passed to fuzzy inference system, where fuzzy membership degrees and reduced set of fuzzy rules play an important part to classify the pixel as noise-free, edge or noisy. Noise-free pixels in the filtering phase remain unaffected causing maximum detail preservation whereas noisy pixels are restored using fuzzy filter. This process is carried out traditionally starting from top left corner of the noisy image to the bottom right corner with a stride rate of one for small input layer and a stride rate of two during convolution. Convolved feature vector is very helpful in finding the edge information and hidden patterns in the input image that are affected by noise. The performance of the proposed study is tested on large data set using standard performance measures and the proposed technique outperforms many existing state of the art techniques with excellent detail preservation and effective noise removal capabilities.

show abstract

Impulse noise reduction using hybrid neuro‐fuzzy filter with improved firefly algorithm from X‐ray bio‐images

Cited by 10 publications

References 38 publications

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Automatic detection of COVID-19 in chest radiographs using serially concatenated deep and handcrafted features

Firefly Algorithm in Biomedical and Health Care: Advances, Issues and Challenges

Convolved Feature Vector Based Adaptive Fuzzy Filter for Image De-Noising

Contact Info

Product

Resources

About