Terahertz Sensing Based on Photonic Crystal Fibers

Photonic-crystal-fiber (PCF) based sensors in the terahertz spectrum have been immensely studied and implemented due to their unique advantages and high sensitivity. At an early stage, conventional and hybrid structured porous core PCF-based sensors were proposed, but the sensitivity was not so high. With the advancement of PCF fabrication technology, hybrid structured hollow-core PCFs have been reported and offer superior sensing characteristics than the previous types. In this chapter, both porous core and hollow-core PCF-based THz sensors are analyzed and the propagation characteristics are explained using terahertz spectrum. Finally, some promising terahertz sensors are studied and compared at the end of this chapter

Hybrid Heterostructures for SPR Biosensor

Surface plasmon resonance (SPR) based biosensors have been enormously studied in the last decade for their better sensitivity. In recent years hybrid heterostructures are getting popularity to implement these SPR biosensors for their superior sensing capability. This chapter demonstrates the details of SPR technology with two recently studied prism-based hybrid heterostructures. These heterostructures are made up of conventional SPR biosensors with two additional layers of recently invented transition metal dichalcogenides, platinum di-selenide (PtSe2), and highly sensitive 2D material, tungsten di-sulfide (WS2). Angular interrogation method is discussed to investigate the sensing capabilities of the sensors which prove the superiority of the Ag-PtSe2-WS2 structure. The sensing capability of this structure has been found at least 1.67 times higher than that of the conventional non-hybrid structures, respectively, with comparable FOM and QF. A comparison table has been provided at the end of this chapter which also shows the impressive performance of the hybrid heterostructures for SPR biosensors. Proper demonstration with a suitable example of this chapter will emphasize the potential use of hybrid heterostructure based SPR biosensors in prospective medical diagnostics and biomedical detection applications

Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm

Funding Information: This work was supported in part by the National Research Foundation of Korea-Grant funded by the Government of Korea (Ministry of Science and ICT) under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through the Faculty Research Program under Grant 20212023Peer reviewedPublisher PD

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim’s blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim’s blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods

Diabetic Retinopathy Identification Using Parallel Convolutional Neural Network Based Feature Extractor and ELM Classifier

Diabetic retinopathy (DR) is an incurable retinal condition caused by excessive blood sugar that, if left untreated, can result in even blindness. A novel automated technique for DR detection has been proposed in this paper. To accentuate the lesions, the fundus images (FIs) were preprocessed using Contrast Limited Adaptive Histogram Equalization (CLAHE). A parallel convolutional neural network (PCNN) was employed for feature extraction and then the extreme learning machine (ELM) technique was utilized for the DR classification. In comparison to the similar CNN structure, the PCNN design uses fewer parameters and layers, which minimizes the time required to extract distinctive features. The effectiveness of the technique was evaluated on two datasets (Kaggle DR 2015 competition (Dataset 1; 34,984 FIs) and APTOS 2019 (3,662 FIs)), and the results are promising. For the two datasets mentioned, the proposed technique attained accuracies of 91.78 % and 97.27 % respectively. However, one of the study's subsidiary discoveries was that the proposed framework demonstrated stability for both larger and smaller datasets, as well as for balanced and imbalanced datasets. Furthermore, in terms of classifier performance metrics, model parameters and layers, and prediction time, the suggested approach outscored existing state-of-the-art models, which would add significant benefit for the medical practitioners in accurately identifying the DR

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively

Fast and accurate fault detection and classification in transmission lines using extreme learning machine

To provide stability and a continuous supply of power, the detection and classification of faults in the transmission lines (TLs) are crucial in this modern age. It is required to remove a faulty section from a healthy section to provide safety and to minimize power loss due to the fault. In the contemporary world, machine learning (ML) is extensively used in every aspect of life. In this study, a spontaneous fault detection (FD) and fault classification (FC) system based on ML has been proposed. MATLAB Simulink was employed to simulate two different TLs and to generate normal and fault data (Per unit voltage and current) of ten different types. TL-1 consisted of a single generator and a single load whereas TL-2 consisted of two generators and three loads. Upon normalizing the data, an extreme learning machine (ELM) algorithm was used as the classifier. Two different ELM models were developed for FD and FC purposes through training. The method achieved fault classification accuracies of 99.18% and 99.09% for the TL-1 and TL-2 respectively. On the other hand, fault detection accuracies of 99.53% and 99.60% were achieved for the TL-1 and TL-2. The proposed ELM model compared to a traditional artificial neural network (ANN) model demonstrated relatively a shorter processing time and reduced computational complexity. In addition, the proposed method outperformed the existing state-of-the-art methods

Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images

Diabetic Retinopathy (DR) is a major complication in human eyes among the diabetic patients. Early detection of the DR can save many patients from permanent blindness. Various artificial intelligent based systems have been proposed and they outperform human analysis in accurate detection of the DR. In most of the traditional deep learning models, the cross-entropy is used as a common loss function in a single stage end-to-end training method. However, it has been recently identified that this loss function has some limitations such as poor margin leading to false results, sensitive to noisy data and hyperparameter variations. To overcome these issues, supervised contrastive learning (SCL) has been introduced. In this study, SCL method, a two-stage training method with supervised contrastive loss function was proposed for the first time to the best of authors' knowledge to identify the DR and its severity stages from fundus images (FIs) using “APTOS 2019 Blindness Detection” dataset. “Messidor-2” dataset was also used to conduct experiments for further validating the model's performance. Contrast Limited Adaptive Histogram Equalization (CLAHE) was applied for enhancing the image quality and the pre-trained Xception CNN model was deployed as the encoder with transfer learning. To interpret the SCL of the model, t-SNE method was used to visualize the embedding space (unit hyper sphere) composed of 128 D space into a 2 D space. The proposed model achieved a test accuracy of 98.36%, and AUC score of 98.50% to identify the DR (Binary classification) and a test accuracy of 84.364%, and AUC score of 93.819% for five stages grading with the APTOS 2019 dataset. Other evaluation metrics (precision, recall, F1-score) were also determined with APTOS 2019 as well as with Messidor-2 for analyzing the performance of the proposed model. It was also concluded that the proposed method achieved better performance in detecting the DR compared to the conventional CNN without SCL and other state-of-the-art methods

A Novel Method for Multivariant Pneumonia Classification based on Hybrid CNN-PCA Based Feature Extraction using Extreme Learning Machine with Chest X-Ray Images

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and timeconsuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network - principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc

2D Nanomaterial-Based Hybrid Structured (Au-WSe2-PtSe2-BP) Surface Plasmon Resonance (SPR) Sensor With Improved Performance

As a promising optical method used in a variety of applications surface plasmon resonance (SPR) sensors are employed over a wide range of boundaries. This research proposes a highly sensitive SPR based sensor with a novel hybrid structure using transition metal dichalcogenides (e.g. WSe 2 , PtSe 2 ) along with black phosphorene (BP) through comprehensive numerical study. To analyze and evaluate the performances of the proposed sensor, the widely used transfer matrix method (TMM) was used. The performances of the sensor were measured in terms of reflectivity, sensitivity, detection accuracy (DA), and figure of merit (FOM). The sensor structure was optimized by changing different structural parameters of the hybrid architecture to obtain better performances. The results revealed that insertion of PtSe 2 with WSe 2 and BP over a gold layer of the conventional structure improved the performance of the sensor and the maximum sensitivity of the sensor was measured as 200 deg/RIU with a FOM of 17.70 RIU −1 . As well, the light penetration through the optimized sensor is investigated using the finite element method (FEM) based software. With this kind of high sensing capabilities, it may be convinced that the proposed sensor can be applied in different fields of biosensing to detect liquid biological and biochemical samples or analytes