Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classif...Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classification.In any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature reduction.The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy.The two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for downsampling.They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors.However,there are parameters which directly affect how a CNN model is trained.In this paper,some issues concerned with the training of CNNs have been handled.The CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of epochs.Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences.Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.展开更多
Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and C...Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16.展开更多
In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutio...In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.展开更多
This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations ...This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations are employed to ensure image detail protection and noise-immunity.The objective of using morphological operations is to remove the defects in the texture of the image.Secondly,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers.The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers.It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix.Simulation results are performed on different medical image modalities.Ultrasonic(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this work.The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation.Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.展开更多
The segmentation process requires separating the image region into sub-regions of similar properties.Each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper sugges...The segmentation process requires separating the image region into sub-regions of similar properties.Each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)algorithms.An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques.The existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed algorithms.This paper extensively analyzes and summarizes the comparative investigation of these techniques.Finally,a prediction of the improvement involves the combination of these techniques is suggested.The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.展开更多
In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission T...In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)techniques.In the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI techniques.SI denoising techniques can be carried out both in a transform or spatial domain.This paper is concerned with single-image noise reduction techniques because we deal with single medical images.The most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are studied.Also,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are investigated.Finally,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities.This model utilizes the Batch Normalization(BN)and the ReLU as a basic structure.As a result,it attained a considerable improvement over the traditional techniques.The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied universally.Based on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.展开更多
This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,efficiently.The presented solution depends on the features of discre...This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,efficiently.The presented solution depends on the features of discrete cosine transform(DCT),lifting wavelet transform(LWT),and singular value decomposition(SVD).The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks.During watermark embedding,the host color medical image is transformed into four sub-bands by employing three stages of LWT.The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation.Furthermore,a fusion process is used for combining different watermarks into a single watermark image.This single fused image is then ciphered using Deoxyribose Nucleic Acid(DNA)encryption to strengthen the security.Then,the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image.The main contribution of this work is embedding multiple watermarks to prevent identity theft.In the presence of different multimedia attacks,several simulation tests on different colormedical images have been performed.The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio(PSNR)values and high correlation between the extracted and original watermark images.Moreover,the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.展开更多
Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive approach.However,the discrete fractional random transform(DFRNT)with adequate basic randomness remains...Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive approach.However,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be examined.This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLM)encryption.First,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive watermarking.To efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal.Moreover,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVM)techniques.The limitations of watermark embedding are also explained to conserve watermarking energy.Second,3D-CLM encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy system.The proposed security system is effective against many types of channel noise attacks,according to simulation results.展开更多
Tuberculosis is one of the most contagious and lethal illnesses in the world,according to the World Health Organization.Tuberculosis had the leading mortality rate as a result of a single infection,ranking above HIV/A...Tuberculosis is one of the most contagious and lethal illnesses in the world,according to the World Health Organization.Tuberculosis had the leading mortality rate as a result of a single infection,ranking above HIV/AIDS.Early detection is an essential factor in patient treatment and can improve the survival rate.Detection methods should have high mobility,high accuracy,fast detection,and low losses.This work presents a novel biomedical photonic crystal fiber sensor,which can accurately detect and distinguish between the different types of tuberculosis bacteria.The designed sensor detects these types with high relative sensitivity and negligible losses compared to other photonic crystal fiber-based biomedical sensors.The proposed sensor exhibits a relative sensitivity of 90.6%,an effective area of 4.342×10^(-8)m^(2),with a negligible confinement loss of 3.13×10^(-9)cm^(-1),a remarkably low effective material loss of 0.0132cm-f,and a numerical aperture of 0.3462.The proposed sensor is capable of operating in the terahertz regimes over a wide range(1 THz-2.4 THz).An abbreviated review of non-optical detection techniques is also presented.An in-depth comparison between this work and recent related photonic crystal fiber-based literature is drawn to validate the efficacy and authenticity of the proposed design.展开更多
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Recently,deep learning(DL)became one of the essential tools in bioinformatics.A modified convolutional neural network(CNN)is employed in this paper for building an integratedmodel for deoxyribonucleic acid(DNA)classification.In any CNN model,convolutional layers are used to extract features followed by max-pooling layers to reduce the dimensionality of features.A novel method based on downsampling and CNNs is introduced for feature reduction.The downsampling is an improved form of the existing pooling layer to obtain better classification accuracy.The two-dimensional discrete transform(2D DT)and two-dimensional random projection(2D RP)methods are applied for downsampling.They convert the high-dimensional data to low-dimensional data and transform the data to the most significant feature vectors.However,there are parameters which directly affect how a CNN model is trained.In this paper,some issues concerned with the training of CNNs have been handled.The CNNs are examined by changing some hyperparameters such as the learning rate,size of minibatch,and the number of epochs.Training and assessment of the performance of CNNs are carried out on 16S rRNA bacterial sequences.Simulation results indicate that the utilization of a CNN based on wavelet subsampling yields the best trade-off between processing time and accuracy with a learning rate equal to 0.0001,a size of minibatch equal to 64,and a number of epochs equal to 20.
基金This work was funded and supported by the Taif University Researchers Supporting Project Number(TURSP-2020/147),Taif University,Taif,Saudi Arabia.
文摘Coronavirus(COVID-19)infection was initially acknowledged as a global pandemic in Wuhan in China.World Health Organization(WHO)stated that the COVID-19 is an epidemic that causes a 3.4%death rate.Chest X-Ray(CXR)and Computerized Tomography(CT)screening of infected persons are essential in diagnosis applications.There are numerous ways to identify positive COVID-19 cases.One of the fundamental ways is radiology imaging through CXR,or CT images.The comparison of CT and CXR scans revealed that CT scans are more effective in the diagnosis process due to their high quality.Hence,automated classification techniques are required to facilitate the diagnosis process.Deep Learning(DL)is an effective tool that can be utilized for detection and classification this type of medical images.The deep Convolutional Neural Networks(CNNs)can learn and extract essential features from different medical image datasets.In this paper,a CNN architecture for automated COVID-19 detection from CXR and CT images is offered.Three activation functions as well as three optimizers are tested and compared for this task.The proposed architecture is built from scratch and the COVID-19 image datasets are directly fed to train it.The performance is tested and investigated on the CT and CXR datasets.Three activation functions:Tanh,Sigmoid,and ReLU are compared using a constant learning rate and different batch sizes.Different optimizers are studied with different batch sizes and a constant learning rate.Finally,a comparison between different combinations of activation functions and optimizers is presented,and the optimal configuration is determined.Hence,the main objective is to improve the detection accuracy of COVID-19 from CXR and CT images using DL by employing CNNs to classify medical COVID-19 images in an early stage.The proposed model achieves a classification accuracy of 91.67%on CXR image dataset,and a classification accuracy of 100%on CT dataset with training times of 58 min and 46 min on CXR and CT datasets,respectively.The best results are obtained using the ReLU activation function combined with the SGDM optimizer at a learning rate of 10−5 and a minibatch size of 16.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘In developing countries,medical diagnosis is expensive and time consuming.Hence,automatic diagnosis can be a good cheap alternative.This task can be performed with artificial intelligence tools such as deep Convolutional Neural Networks(CNNs).These tools can be used on medical images to speed up the diagnosis process and save the efforts of specialists.The deep CNNs allow direct learning from the medical images.However,the accessibility of classified data is still the largest challenge,particularly in the field of medical imaging.Transfer learning can deliver an effective and promising solution by transferring knowledge from universal object detection CNNs to medical image classification.However,because of the inhomogeneity and enormous overlap in intensity between medical images in terms of features in the diagnosis of Pneumonia and COVID-19,transfer learning is not usually a robust solution.Single-Image Super-Resolution(SISR)can facilitate learning to enhance computer vision functions,apart from enhancing perceptual image consistency.Consequently,it helps in showing the main features of images.Motivated by the challenging dilemma of Pneumonia and COVID-19 diagnosis,this paper introduces a hybrid CNN model,namely SIGTra,to generate super-resolution versions of X-ray and CT images.It depends on aGenerative Adversarial Network(GAN)for the super-resolution reconstruction problem.Besides,Transfer learning with CNN(TCNN)is adopted for the classification of images.Three different categories of chest X-ray and CT images can be classified with the proposed model.A comparison study is presented between the proposed SIGTra model and the other relatedCNNmodels for COVID-19 detection in terms of precision,sensitivity,and accuracy.
文摘This paper presents a study of the segmentation of medical images.The paper provides a solid introduction to image enhancement along with image segmentation fundamentals.In the first step,the morphological operations are employed to ensure image detail protection and noise-immunity.The objective of using morphological operations is to remove the defects in the texture of the image.Secondly,the Fuzzy C-Means(FCM)clustering algorithm is used to modify membership function based only on the spatial neighbors instead of the distance between pixels within local spatial neighbors and cluster centers.The proposed technique is very simple to implement and significantly fast since it is not necessary to compute the distance between the neighboring pixels and the cluster centers.It is also efficient when dealing with noisy images because of its ability to efficiently improve the membership partition matrix.Simulation results are performed on different medical image modalities.Ultrasonic(Us),X-ray(Mammogram),Computed Tomography(CT),Positron Emission Tomography(PET),and Magnetic Resonance(MR)images are the main medical image modalities used in this work.The obtained results illustrate that the proposed technique can achieve good results with a short time and efficient image segmentation.Simulation results on different image modalities show that the proposed technique can achieve segmentation accuracies of 98.83%,99.71%,99.83%,99.85%,and 99.74%for Us,Mammogram,CT,PET,and MRI images,respectively.
文摘The segmentation process requires separating the image region into sub-regions of similar properties.Each sub-region has a group of pixels having the same characteristics,such as texture or intensity.This paper suggests an efficient hybrid segmentation approach for different medical image modalities based on particle swarm optimization(PSO)and improved fast fuzzy C-means clustering(IFFCM)algorithms.An extensive comparative study on different medical images is presented between the proposed approach and other different previous segmentation techniques.The existing medical image segmentation techniques incorporate clustering,thresholding,graph-based,edge-based,active contour,region-based,and watershed algorithms.This paper extensively analyzes and summarizes the comparative investigation of these techniques.Finally,a prediction of the improvement involves the combination of these techniques is suggested.The obtained results demonstrate that the proposed hybrid medical image segmentation approach provides superior outcomes in terms of the examined evaluation metrics compared to the preceding segmentation techniques.
文摘In the literature,numerous techniques have been employed to decrease noise in medical image modalities,including X-Ray(XR),Ultrasonic(Us),Computed Tomography(CT),Magnetic Resonance Imaging(MRI),and Positron Emission Tomography(PET).These techniques are organized into two main classes:the Multiple Image(MI)and the Single Image(SI)techniques.In the MI techniques,images usually obtained for the same area scanned from different points of view are used.A single image is used in the entire procedure in the SI techniques.SI denoising techniques can be carried out both in a transform or spatial domain.This paper is concerned with single-image noise reduction techniques because we deal with single medical images.The most well-known spatial domain noise reduction techniques,including Gaussian filter,Kuan filter,Frost filter,Lee filter,Gabor filter,Median filter,Homomorphic filter,Speckle reducing anisotropic diffusion(SRAD),Nonlocal-Means(NL-Means),and Total Variation(TV),are studied.Also,the transform domain noise reduction techniques,including wavelet-based and Curvelet-based techniques,and some hybridization techniques are investigated.Finally,a deep(Convolutional Neural Network)CNN-based denoising model is proposed to eliminate Gaussian and Speckle noises in different medical image modalities.This model utilizes the Batch Normalization(BN)and the ReLU as a basic structure.As a result,it attained a considerable improvement over the traditional techniques.The previously mentioned techniques are evaluated and compared by calculating qualitative visual inspection and quantitative parameters like Peak Signal-to-Noise Ratio(PSNR),Correlation Coefficient(Cr),and system complexity to determine the optimum denoising algorithm to be applied universally.Based on the quality metrics,it is demonstrated that the proposed deep CNN-based denoising model is efficient and has superior denoising performance over the traditionaldenoising techniques.
文摘This paper presents a robust multi-stage security solution based on fusion,encryption,and watermarking processes to transmit color healthcare images,efficiently.The presented solution depends on the features of discrete cosine transform(DCT),lifting wavelet transform(LWT),and singular value decomposition(SVD).The primary objective of this proposed solution is to ensure robustness for the color medical watermarked images against transmission attacks.During watermark embedding,the host color medical image is transformed into four sub-bands by employing three stages of LWT.The resulting low-frequency sub-band is then transformed by employing three stages of DCT followed by SVD operation.Furthermore,a fusion process is used for combining different watermarks into a single watermark image.This single fused image is then ciphered using Deoxyribose Nucleic Acid(DNA)encryption to strengthen the security.Then,the DNA-ciphered fused watermark is embedded in the host medical image by applying the suggested watermarking technique to obtain the watermarked image.The main contribution of this work is embedding multiple watermarks to prevent identity theft.In the presence of different multimedia attacks,several simulation tests on different colormedical images have been performed.The results prove that the proposed security solution achieves a decent imperceptibility quality with high Peak Signal-to-Noise Ratio(PSNR)values and high correlation between the extracted and original watermark images.Moreover,the watermark image extraction process succeeds in achieving high efficiency in the presence of attacks compared with related works.
基金Project supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project(No.PNURSP2023R66)。
文摘Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive approach.However,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be examined.This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLM)encryption.First,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive watermarking.To efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal.Moreover,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVM)techniques.The limitations of watermark embedding are also explained to conserve watermarking energy.Second,3D-CLM encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy system.The proposed security system is effective against many types of channel noise attacks,according to simulation results.
文摘Tuberculosis is one of the most contagious and lethal illnesses in the world,according to the World Health Organization.Tuberculosis had the leading mortality rate as a result of a single infection,ranking above HIV/AIDS.Early detection is an essential factor in patient treatment and can improve the survival rate.Detection methods should have high mobility,high accuracy,fast detection,and low losses.This work presents a novel biomedical photonic crystal fiber sensor,which can accurately detect and distinguish between the different types of tuberculosis bacteria.The designed sensor detects these types with high relative sensitivity and negligible losses compared to other photonic crystal fiber-based biomedical sensors.The proposed sensor exhibits a relative sensitivity of 90.6%,an effective area of 4.342×10^(-8)m^(2),with a negligible confinement loss of 3.13×10^(-9)cm^(-1),a remarkably low effective material loss of 0.0132cm-f,and a numerical aperture of 0.3462.The proposed sensor is capable of operating in the terahertz regimes over a wide range(1 THz-2.4 THz).An abbreviated review of non-optical detection techniques is also presented.An in-depth comparison between this work and recent related photonic crystal fiber-based literature is drawn to validate the efficacy and authenticity of the proposed design.
