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In the present investigation, rotary friction weld joints were prepared between carbon steel and low alloy steel pipes. These types of similar and dissimilar configurations are in general, used for drill pipe joining. Low alloy steel for the friction welding in dissimilar configurations were used in two different microstructure state; a) as-received and b) quenched and tempered. The friction weld joints showed three distinct weld microstructure characteristics; a) radial plastic flow, b) axial plastic flow and, c) partial heat affected zone, for joining configurations. In general, microstructure characterization revealed grain size refinement and carbide precipitation in the vicinity of bond interface for all types of similar and dissimilar steel joining conditions. A characteristic bond layer width of ~2 μm was developed between the dissimilar steel weld joints whereas it was found to be absent for the similar weld joints. These weld joints were evaluated for their mechanical properties by using conventional tensile tests and furthermore by automated ball indentation technique. The study has also shown that in the case of dissimilar steel joints the mechanical properties of the bond interface can be obtained aptly by using an automated ball indentation technique. The quench and temper treatment given to the low alloy steel resulted in the improved bond interface strength. The improvement of the mechanical properties of the weld joints was attributed to the synergistic effect of grain size refinement and tempered martensite microstructure. [ABSTRACT FROM AUTHOR]

This study delves into the Wire Arc directed energy Deposition (WADED) process, exploring the impact of four tool path trajectories designated as bidirectional contour (S1), bidirectional spiral (S2), Fermat's spiral (S3), and Fermat's zigzag (S4) on the deposition of a regular cubic solid structure of NiCrMo-3 alloy. The research focuses on understanding how these trajectories influence both microstructural heterogeneity and anisotropic mechanical response. It has been shown that the WADED processed NiCrMo-3 alloy displays prominent dendritic microstructure, with varying dendritic arm spacing and growth orientation angle across S1 to S4. S2 and S4 favours more columnar to equiaxed transition due to relatively lower cooling rates and G/R values along with higher extent of Laves and carbide precipitation compared to S1 and S3. Texture analysis reveals S1 and S3 exhibit a predominant cube {001} 〈100〉 texture, while S2 transitions to Goss {110} 〈001〉 texture, and S4 displays a more random texture with higher intensity in copper {112} 〈111〉 and brass {110} 〈1−12〉. Microhardness exhibited slight anisotropy (within 3%), with S4 (227.77HV0.5) having the highest microhardness. The YS and UTS were lower in the build direction (〈001〉 S) compared to the horizontal direction (<001 > S) in all samples. S2 showcased maximum YS (334.5 MPa) and UTS (655 MPa) values while minimum anisotropy (≤2%) was observed in S4 along with comparable YS (330 MPa) and UTS (645.5 MPa). • Tool path trajectories impact microstructure and mechanical anisotropic behaviour of NiCrMo-3 alloy. • The d and θ are higher along the build direction than the horizontal direction. • Lower grain growth anisotropy promotes transition from columnar to equiaxed dendritic growth. • Higher anisotropy creates mixed orientations of <001> + <101> and < 001> + <101> + <111>. • Anisotropy is observed in the tensile properties, with decreasing anisotropy in YS and UTS from S1 to S4. [ABSTRACT FROM AUTHOR]

[Display omitted] • CePO4-based chemical conversion coating on magnesium AZ31 alloy was developed. • Preferential deposition of CePO4 occurred when ethanol was used as the solvent. • Cerium hydroxide/oxides dominated over phosphates in the coating prepared using water. • The highest corrosion resistance (NaCl) was obtained for the coatings formed in ethanol. • Coatings prepared using ethanol exhibited good biocompatibility and biocorrosion. A two-stage chemical conversion coating procedure was proposed for cerium phosphate based coating on Mg alloys. Initially, a layer of magnesium phosphate was obtained and the cerium phosphate based coatings were achieved by immersing the precoated sample in a cerium nitrate solution prepared in either water or ethanol. The resultant coating obtained from water based nitrate bath consisted of oxides and hydroxides of cerium in addition to cerium phosphate. On the other hand, due to the lack of OH– ions in ethanol based nitrate bath, oxides and hydroxides were suppressed and cerium phosphate rich coating was obtained. Scratch test and scanning vibrating electrode analysis confirmed the self-healing characteristics of the water based coating due to the presence of cerium oxide and hydroxide in the coating. The detailed corrosion studies revealed that the ethanol based coating exhibited superior corrosion resistance in both 1 wt% NaCl as well as in simulated body fluid due to the high stability of cerium phosphate in both test conditions. Both the coatings displayed acceptable cell viability (MG63 cell line) under physiological conditions and hence have the potential for both engineering and biomedical applications. [ABSTRACT FROM AUTHOR]

Examination of the brain's condition with the Electroencephalogram (EEG) can be helpful to predict abnormality and cerebral activities. The purpose of this study was to develop an Automated Diagnostic Tool (ADT) to investigate and classify the EEG signal patterns into normal and schizophrenia classes. The ADT implements a sequence of events, such as EEG series splitting, non-linear features mining, t-test assisted feature selection, classification and validation. The proposed ADT is employed to evaluate a 19-channel EEG signal collected from normal and schizophrenia class volunteers. A dataset was created by splitting the raw 19-channel EEG into a sequence of 6250 sample points, which was helpful to produce 1142 features of normal and schizophrenia class patterns. Non-linear feature extraction was then implemented to mine 157 features from each EEG pattern, from which 14 of the principal features were identified based on significance. Finally, a signal classification practice with Decision-Tree (DT), Linear-Discriminant analysis (LD), k-Nearest-Neighbour (KNN), Probabilistic-Neural-Network (PNN), and Support-Vector-Machine (SVM) with various kernels was implemented. The experimental outcome showed that the SVM with Radial-Basis-Function (SVM-RBF) offered a superior average performance value of 92.91% on the considered EEG dataset, as compared to other classifiers implemented in this work. [ABSTRACT FROM AUTHOR]

A dragline is the largest mobile equipment on earth, and it is called the "kingpin" of any mine site. In this present investigation, a case of premature failure of a coal-handling dragline is discussed. Failure occurred in a linked component known as "thimble," which connects the A-frame and the vertical mast of the dragline. The thimble was broken into two halves and caused the entire front structures of the dragline to collapse. Failure investigation was performed through visual examination, chemical analysis, metallography, mechanical property evaluation and fractography. [ABSTRACT FROM AUTHOR]

The present work deals with the development of temperature and magnetic responsive hydrogel networks based on poly (N-isopropylacrylamide)/acrylamido propane sulfonic acid. The hydrogel matrices are synthesized by polymerizing N-isopropylacrylamide (NIPAM) monomer in the presence of acrylamido propane sulphonicacid (AMPS) using a cross-linker ( N , N -methylenebisacrylamide, MBA) and redox initiating system [ammonium persulphate (APS)/tetramethylethylenediamine (TMEDA)]. The magnetic nanoparticles are generated throughout the hydrogel networks using in situ method by incorporating iron ions and subsequent treatment with ammonia. A series of hydrogel-magnetic nanocomposites (HGMNC) are developed by varying AMPS composition. The synthesized hydrogel magnetic nanocomposites (HGMNC) are characterized by using Fourier Transform Infrared (FTIR) Spectroscopy, X-ray diffraction (XRD), Thermal Analyses and Electron Microscopy analysis (Scanning and Transmission Electron Microscope). The metal extraction capacities of the prepared hydrogel (HG) and hydrogel magnetic nanocomposites (HGMNC) were studied at different temperatures. The results suggest that HGMNCs have higher extraction capacity compared to HG and HG loaded iron ions. This data also reveals that the extraction of metals by hydrogel magnetic nanocomposites (HGMNCs) is higher at higher temperatures than room temperature. The prepared HGMNCs are also subjected to hyperthermia (cancer therapy) studies. [ABSTRACT FROM AUTHOR]

The energy-efficient quenching and nonisothermal partitioning process has been shown to engender an excellent strength-ductility combination in low alloy steels. Conventionally, these steels are quenched to a specific temperature in the martensite region, i.e. between M s and M f , followed by slow cooling to room temperature. However, in the present study, an attempt has been made to investigate the behaviour of low alloy steel quenched in the bainite region, i.e. temperature between B s and M s , followed by slow cooling to room temperature. For comparison, the same steel was also quenched to two different temperatures in the martensite region as well as direct quenched to room temperature. The XRD results showed the maximum austenite retention for a lower quench temperature in the martensite region. Both the direct quenching and the quenching and nonisothermal partitioning from the martensite region led to a microstructure dominated by the presence of auto-tempered martensitic laths. However, the sample quenched in the bainite region and subsequent slow cooling gave rise to bainite-ferrite laths containing coarse carbides. An interlath precipitation of carbides was observed for the bainite, in contrast to the intralath carbide precipitation in martensite. The austenite remained at the quench temperature decomposed to bainite during slow cooling from the bainite region, whereas M-A constituents were formed during slow cooling from the martensite region. The microstructural constituents were observed to be finest for the direct-quenched sample due to a lower quench temperature. Interestingly, the sample quenched in the bainite region showed a significant improvement in ductility, in contrast to the sample quenched in the martensite region and direct-quenched to room temperature that showed higher strength. • Unravelling the importance of quenching strategy and ensuing nonisothermal partitioning. • Thermodynamic and kinetic assessment of displacive multiphase transformations. • Correlation of microstructural observations with mechanical behaviour and fracture mechanisms. • Two-fold improvement in the ductility through bainite-based quenching and nonisothermal partitioning process. [ABSTRACT FROM AUTHOR]

Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process. This research aims in developing a simple DAS for COVID-19 detection using the pre-trained lightweight deep learning methods (LDMs) applied to lung CT slices. The use of LDMs contributes to a less complex yet highly accurate detection system. The key stages of the developed DAS include image collection and initial processing using Shannon's thresholding, deep-feature mining supported by LDMs, feature optimization utilizing the Brownian Butterfly Algorithm (BBA), and binary classification through three-fold cross-validation. The performance evaluation of the proposed scheme involves assessing individual, fused, and ensemble features. The investigation reveals that the developed DAS achieves a detection accuracy of 93.80% with individual features, 96% accuracy with fused features, and an impressive 99.10% accuracy with ensemble features. These outcomes affirm the effectiveness of the proposed scheme in significantly enhancing COVID-19 detection accuracy in the chosen lung CT database., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of stroke symptoms is essential to prevent strokes and promote a healthy lifestyle. FAST tests (looking for abnormalities in the face, arms, and speech) have limitations in reliability and accuracy for diagnosing strokes. This research employs machine learning (ML) techniques to develop and assess multiple ML models to establish a robust stroke risk prediction framework. This research uses a stacking-based ensemble method to select the best three machine learning (ML) models and combine their collective intelligence. An empirical evaluation of a publicly available stroke prediction dataset demonstrates the superior performance of the proposed stacking-based ensemble model, with only one misclassification. The experimental results reveal that the proposed stacking model surpasses other state-of-the-art research, achieving accuracy, precision, F1-score of 99.99%, recall of 100%, receiver operating characteristics (ROC), Mathews correlation coefficient (MCC), and Kappa scores 1.0. Furthermore, Shapley's Additive Explanations (SHAP) are employed to analyze the predictions of the black-box machine learning (ML) models. The findings highlight that age, BMI, and glucose level are the most significant risk factors for stroke prediction. These findings contribute to the development of more efficient techniques for stroke prediction, potentially saving many lives.

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images. The research encompasses four stages: (i) Image collection and preprocessing, gathering and preparing histopathology images for analysis; (ii) feature extraction using deep and handcrafted scheme, extracting relevant features from images using deep learning techniques and traditional methods; (iii) feature reduction artificial hummingbird algorithm (AHA) and concatenation: Reducing feature dimensionality using AHA and concatenating them serially and (iv) binary classification and performance validation with three-fold cross-validation: Classifying images as healthy or oral squamous cell carcinoma and evaluating the framework's performance using three-fold cross-validation. The current study examined whole slide biopsy images at 100× and 400× magnifications. To establish OralNet's validity, 3000 cropped and resized images were reviewed, comprising 1500 healthy and 1500 oral squamous cell carcinoma images. Experimental results using OralNet achieved an oral cancer detection accuracy exceeding 99.5%. These findings confirm the clinical significance of the proposed technique in detecting oral cancer presence in histology slides.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme. The reliability of the developed scheme is also verified through clinically collected MRI slices. In the proposed scheme, the following stages are involved: (i) pre-processing the raw MRI image, (ii) deep-feature extraction using pretrained schemes, (iii) watershed-algorithm-based BT segmentation and mining the shape features, (iv) feature optimization using the elephant-herding algorithm (EHA), and (v) binary classification and verification using three-fold cross-validation. Using (a) individual features, (b) dual deep features, and (c) integrated features, the BT-classification task is accomplished in this study. Each experiment is conducted separately on the chosen BRATS and TCIA benchmark MRI slices. This research indicates that the integrated feature-based scheme helps to achieve a classification accuracy of 99.6667% when a support-vector-machine (SVM) classifier is considered. Further, the performance of this scheme is verified using noise-attacked MRI slices, and better classification results are achieved.

Introduction: Cancer happening rates in humankind are gradually rising due to a variety of reasons, and sensible detection and management are essential to decrease the disease rates. The kidney is one of the vital organs in human physiology, and cancer in the kidney is a medical emergency and needs accurate diagnosis and well-organized management., Methods: The proposed work aims to develop a framework to classify renal computed tomography (CT) images into healthy/cancer classes using pre-trained deep-learning schemes. To improve the detection accuracy, this work suggests a threshold filter-based pre-processing scheme, which helps in removing the artefact in the CT slices to achieve better detection. The various stages of this scheme involve: (i) Image collection, resizing, and artefact removal, (ii) Deep features extraction, (iii) Feature reduction and fusion, and (iv) Binary classification using five-fold cross-validation., Results and Discussion: This experimental investigation is executed separately for: (i) CT slices with the artefact and (ii) CT slices without the artefact. As a result of the experimental outcome of this study, the K-Nearest Neighbor (KNN) classifier is able to achieve 100% detection accuracy by using the pre-processed CT slices. Therefore, this scheme can be considered for the purpose of examining clinical grade renal CT images, as it is clinically significant., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Rajinikanth, Vincent, Srinivasan, Ananth Prabhu and Chang.)

Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients’ brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided >91%, HF obtained >85%, and DF+HF achieved >95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients’ brain MRI slices.

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB ® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier.

Lung abnormality in humans is steadily increasing due to various causes, and early recognition and treatment are extensively suggested. Tuberculosis (TB) is one of the lung diseases, and due to its occurrence rate and harshness, the World Health Organization (WHO) lists TB among the top ten diseases which lead to death. The clinical level detection of TB is usually performed using bio-medical imaging methods, and a chest X-ray is a commonly adopted imaging modality. This work aims to develop an automated procedure to detect TB from X-ray images using VGG-UNet-supported joint segmentation and classification. The various phases of the proposed scheme involved; (i) image collection and resizing, (ii) deep-features mining, (iii) segmentation of lung section, (iv) local-binary-pattern (LBP) generation and feature extraction, (v) optimal feature selection using spotted hyena algorithm (SHA), (vi) serial feature concatenation, and (vii) classification and validation. This research considered 3000 test images (1500 healthy and 1500 TB class) for the assessment, and the proposed experiment is implemented using Matlab®. This work implements the pretrained models to detect TB in X-rays with improved accuracy, and this research helped achieve a classification accuracy of >99% with a fine-tree classifier., Competing Interests: The authors declare that they have no conflicts of interest to report regarding the present study., (Copyright © 2022 Seifedine Kadry et al.)

This research work aims to implement an automated segmentation process to extract the endoplasmic reticulum (ER) network in fluorescence microscopy images (FMI) using pretrained convolutional neural network (CNN). The threshold level of the raw FMT is complex, and extraction of the ER network is a challenging task. Hence, an image conversion procedure is initially employed to reduce its complexity. This work employed the pretrained CNN schemes, such as VGG-UNet and VGG-SegNet, to mine the ER network from the chosen FMI test images. The proposed ER segmentation pipeline consists of the following phases; (i) clinical image collection, 16-bit to 8-bit conversion and resizing; (ii) implementation of pretrained VGG-UNet and VGG-SegNet; (iii) extraction of the binary form of ER network; (iv) comparing the mined ER with ground-truth; and (v) computation of image measures and validation. The considered FMI dataset consists of 223 test images, and image augmentation is then implemented to increase these images. The result of this scheme is then confirmed against other CNN methods, such as U-Net, SegNet, and Res-UNet. The experimental outcome confirms a segmentation accuracy of >98% with VGG-UNet and VGG-SegNet. The results of this research authenticate that the proposed pipeline can be considered to examine the clinical-grade FMI., Competing Interests: The authors declare no conflict of interest., (Copyright © 2022 Jesline Daniel et al.)

Understanding the reason for an infant's cry is the most difficult thing for parents. There might be various reasons behind the baby's cry. It may be due to hunger, pain, sleep, or diaper-related problems. The key concept behind identifying the reason behind the infant's cry is mainly based on the varying patterns of the crying audio. The audio file comprises many features, which are highly important in classifying the results. It is important to convert the audio signals into the required spectrograms. In this article, we are trying to find efficient solutions to the problem of predicting the reason behind an infant's cry. In this article, we have used the Mel-frequency cepstral coefficients algorithm to generate the spectrograms and analyzed the varying feature vectors. We then came up with two approaches to obtain the experimental results. In the first approach, we used the Convolution Neural network (CNN) variants like VGG16 and YOLOv4 to classify the infant cry signals. In the second approach, a multistage heterogeneous stacking ensemble model was used for infant cry classification. Its major advantage was the inclusion of various advanced boosting algorithms at various levels. The proposed multistage heterogeneous stacking ensemble model had the edge over the other neural network models, especially in terms of overall performance and computing power. Finally, after many comparisons, the proposed model revealed the virtuoso performance and a mean classification accuracy of up to 93.7%., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Joshi, Srinivasan, Vincent, Rajinikanth and Chang.)

The excessive number of COVID-19 cases reported worldwide so far, supplemented by a high rate of false alarms in its diagnosis using the conventional polymerase chain reaction method, has led to an increased number of high-resolution computed tomography (CT) examinations conducted. The manual inspection of the latter, besides being slow, is susceptible to human errors, especially because of an uncanny resemblance between the CT scans of COVID-19 and those of pneumonia, and therefore demands a proportional increase in the number of expert radiologists. Artificial intelligence-based computer-aided diagnosis of COVID-19 using the CT scans has been recently coined, which has proven its effectiveness in terms of accuracy and computation time. In this work, a similar framework for classification of COVID-19 using CT scans is proposed. The proposed method includes four core steps: (i) preparing a database of three different classes such as COVID-19, pneumonia, and normal; (ii) modifying three pretrained deep learning models such as VGG16, ResNet50, and ResNet101 for the classification of COVID-19-positive scans; (iii) proposing an activation function and improving the firefly algorithm for feature selection; and (iv) fusing optimal selected features using descending order serial approach and classifying using multiclass supervised learning algorithms. We demonstrate that once this method is performed on a publicly available dataset, this system attains an improved accuracy of 97.9% and the computational time is almost 34 (sec)., Competing Interests: All authors declare that they have no conflict of interest in this work., (Copyright © 2021 Hassaan Haider Syed et al.)

Amyotrophic Lateral Sclerosis (ALS) is a disorder of the neuromuscular system that causes the impairment of nerve cells from brain to spinal cord and to the voluntary muscles in every part of the human physiological system, which totally leads to paralysis. The examination of ALS using Electromyograms (EMG) is a challenging task which requires experts to investigate and diagnose. Hence, the development of an efficient and automated procedure is significant for the analysis of ALS signals. In this work, eighty time-frequency features were extricated from EMG signals transformed into time-frequency images. Further, fifteen highly substantial features were chosen using the firefly algorithm with fractional position update. Further, fractional firefly neural network is introduced and developed to examine the EMG signals. The performance metrics of the fractional firefly based neural network diagnostic system were analyzed with different fractional orders (α) and hidden neurons. Results demonstrated that the proposed technique is highly efficient and yields good statistical significance. Further, the accuracy of the fractional firefly neural network classifier with α  = 0.5 and 15 hidden neurons is higher (93.3%) when compared to the accuracy of the classifier with different α values and hidden neurons. The proposed fractional order-based feature selection algorithm and classifier model are highly suitable for development of systems for evaluation of ALS and normal EMG signals, since the proficient discrimination of normal and ALS EMG signals is essential for the identification of neuromuscular disorders., (© 2021. Australasian College of Physical Scientists and Engineers in Medicine.)

Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.

Lung abnormality is one of the common diseases in humans of all age group and this disease may arise due to various reasons. Recently, the lung infection due to SARS-CoV-2 has affected a larger human community globally, and due to its rapidity, the World-Health-Organisation (WHO) declared it as pandemic disease. The COVID-19 disease has adverse effects on the respiratory system, and the infection severity can be detected using a chosen imaging modality. In the proposed research work; the COVID-19 is detected using transfer learning from CT scan images decomposed to three-level using stationary wavelet. A three-phase detection model is proposed to improve the detection accuracy and the procedures are as follows; Phase1- data augmentation using stationary wavelets, Phase2- COVID-19 detection using pre-trained CNN model and Phase3- abnormality localization in CT scan images. This work has considered the well known pre-trained architectures, such as ResNet18, ResNet50, ResNet101, and SqueezeNet for the experimental evaluation. In this work, 70% of images are considered to train the network and 30% images are considered to validate the network. The performance of the considered architectures is evaluated by computing the common performance measures. The result of the experimental evaluation confirms that the ResNet18 pre-trained transfer learning-based model offered better classification accuracy (training = 99.82%, validation = 97.32%, and testing = 99.4%) on the considered image dataset compared with the alternatives., (© Springer Science+Business Media, LLC, part of Springer Nature 2020.)

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection., Competing Interests: Conflict of InterestAll authors declare that they have no conflict of interest., (© The Author(s) 2020.)