Medical Image Retrieval Research Articles

Content-based medical image retrieval with opponent class adaptive margin loss

The increasing utilization of medical imaging technology with digital storage capabilities has facilitated the compilation of large-scale data repositories. Fast access to image samples with similar appearance to suspected cases in these repositories can help establish a consulting system for healthcare professionals, and improve diagnostic procedures while minimizing processing delays. However, manual querying of large repositories is labor intensive. Content-based image retrieval (CBIR) offers an automated solution based on quantitative assessment of image similarity based on image features in a latent space. Since conventional methods based on hand-crafted features typically show poor generalization performance, learning-based CBIR methods have received attention recently. A common framework in this domain involves classifier-guided models that are trained to detect different image classes. Similarity assessments are then performed on the features captured by the intermediate stages of the trained models. While classifier-guided methods are powerful in inter-class discrimination, they are suboptimally sensitive to within-class differences in image features. An alternative framework instead performs task-agnostic training to learn an embedding space that enforces the representational discriminability of images. Within this representational-learning framework, a powerful method is triplet-wise learning that addresses the deficiencies of point-wise and pair-wise learning in characterizing the similarity relationships between image classes. However, the traditional triplet loss enforces separation between only a subset of image samples within the triplet via a manually-set constant margin value, so it can lead to suboptimal segregation of opponent classes and limited generalization performance. To address these limitations, we introduce a triplet-learning method for automated querying of medical image repositories based on a novel Opponent Class Adaptive Margin (OCAM) loss. To maintain optimally discriminative representations, OCAM considers relationships among all image pairs within the triplet and utilizes an adaptive margin value that is automatically selected per dataset and during the course of training iterations. CBIR performance of OCAM is compared against state-of-the-art loss functions for representational learning on three public databases (gastrointestinal disease, skin lesion, lung disease). On average, OCAM shows an mAP performance of 86.30% in the KVASIR dataset, 70.30% in the ISIC 2019 dataset, and 85.57% in the X-RAY dataset. Comprehensive experiments in each application domain demonstrate the superior performance of OCAM against competing triplet-wise methods at 1.52%, classifier-guided methods at 2.29%, and non-triplet representational-learning methods at 4.56%.

Improved Transformer Based Hashing for Cased-based Pneumonia Retrieval

The deep hash method has been proved to be the most effective nearest neighbor search technique in large-scale image retrieval. However, up to now, deep hashing in medical image retrieval has been dominated by convolution network architecture, such as Resnet. Inspired by Transformer, we integrate Transformer into the deep learning framework. Our framework is mainly composed of two main modules: (1) In order to solve the problem of big difference among different subclasses of medical image categories, the Transformer model is improved in this paper. The external attention is used to replace the self-attention in the original Transformer model, and the feature acquisition ability of the model is improved by capturing the correlation between samples. (2) In addition, in order to learn compact binary coding, we adopt Bayesian framework for end-to-end learning. We conduct experiments on ChestX-ray Pneumonia dataset, and compare the proposed algorithm with the commonly used depth hashing method. Experimental results show that the MAP values under different hash bit codes are better than the existing deep hash methods, which proves the effectiveness of the proposed method.

Feature Extraction Method using HoG with LTP for Content-Based Medical Image Retrieval

An accurate diagnosis is significant for the treatment of any disease in its early stage. Content-Based Medical Image Retrieval (CBMIR) is used to find similar medical images in a huge database to help radiologists in diagnosis. The main difficulty in CBMIR is semantic gaps between the lower-level visual details, captured by computer-aided tools and higher-level semantic details captured by humans. Many existing methods such as Manhattan Distance, Triplet Deep Hashing, and Transfer Learning techniques for CBMIR were developed but showed lower efficiency and the computational cost was high. To solve such issues, a new feature extraction approach is proposed using Histogram of Gradient (HoG) with Local Ternary Pattern (LTP) to automatically retrieve medical images from the Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) database. Adam optimization algorithm is utilized to select features and the Euclidean measure calculates the similarity for query images. From the experimental analysis, it is clearly showing that the proposed HoG-LTP method achieves higher accuracy of 98.8%, a sensitivity of 98.5%, and a specificity of 99.416%, which is better when compared to the existing Random Forest (RF) method which displayed an accuracy, sensitivity, and specificity of 81.1%, 81.7% and 90.5% respectively.

Performance Evaluation of State-of-the-Art Texture Feature Extraction Techniques on Medical Imagery Tasks

Interpreting medical images is certainly a complex task which requires extensive knowledge [1]. According to Computer Aided Diagnosis (CAD) serves as a second opinion that will help radiologists in diagnosis and on the other hand Content-based Image Retrieval uses visual content to help users browse, search and retrieve similar medical images from a database based on the user’s interest [2-4]. The competency of the CBMIR system depends on feature extraction methods [5]. The textural features are very important to determine the content of a medical image. Textural features provide scenic depth, the spatial distribution of tonal variation, and surface orientation [6]. Therefore, this study seeks to compare and evaluate some of the hand-crafted texture feature extraction techniques in CBMIR. This is to help those concerned in enhancing CBIR systems to make informed decisions concerning the selection of the best textural feature extraction techniques. Since there is no clear indication of which of the various texture feature extraction techniques is best suited for a given performance metric when considering which of the techniques to choose for a particular study in CBMIR systems. The objective of this work, therefore, is to comparatively evaluate the performance of the following texture feature extraction techniques; Local Binary Pattern (LBP), Gabor Filter, Gray-Level Co-occurrence Matrix (GLCM), Haralick Descriptor, Features from Accelerated Segment Test (FAST) and Features from Accelerated Segment Test and Binary Robust Independent Elementary Features (FAST &BRIEF) using the metrics; precision, recall, f1-score, mean squared error (MSE), accuracy and time. These techniques are coupled with specific similarity measure to obtain results. The results showed that LBP, Haralick Descriptor, FAST, and GLCM had the best results in terms of (Precision and Accuracy), Time, F1-Score, and Recall respectively. LBP had 82.05% and 88.23% scores for precision and accuracy respectively. The following scores represent the performance of the Haralick descriptor, FAST, and GLCM models respectively; 0.88s, 38.7%, and 44.82%. These test scores are obtained from datasets ranging from 1k-10.5k. Aside from LBP outperforming the other 5 models mentioned, it still outperformed the following proposed models ' [7]’, ‘Tamura texture feature and wavelet transform combined with Hausdorff distance - [8]’, ‘ [9]’ in terms of (precision, accuracy, and recall) and (precision and recall) respectively and probably f1-score (since f1-score is the weighted average of precision and recall). It is believed that an ensemble of LBP, Haralick descriptors, and Support Vector Machine (SVM) can represent a robust system for both medical image retrieval and classification.

Efficient medical image retrieval system using Geometric Invariant Point Bilateral Transformation (GIPBT)

Sensor rich environments assist CBIR system to provide granularity in detail for many applications. From sensor rich environments, CBIR can provide the process to exploit images that can integrate both search & retrieval mechanisms. Era of digital technology with Smart devices connected to internet so as to communicate is made possible with Internet of Things (IoT). As IoT deals with huge data in the form of images or videos, to be communicated between smart devices through IoT, deals with complex data processing. Proposed Image Retrieval System reduces the computation overhead for wide range of IoT applications. Medical Images are not only image which holds the pixel also the valuable information of medical diagnosis of every human being. Medical image retrieval has become an inescapable task in every hospital IT system due to the influx of medical pictures. A novel texture fusion technique and a rapid similarity measure metric were used in this study to create an efficient medical CBIR. Proposed feature extraction method of Geometric Invariant Point Bilateral Transformation (GIPBT) algorithm provides the texture information. In this method features are computed as point-to-point geometric information of bilateral filtered image. It automatically inoculates the universal information into the local descriptor and reinforces the feature depiction. To further enhance the descriptor, we used Histogram of Second Order Gradient (HSOG) feature extraction method. By use of these two features, we are performing the feature matching. To analyze and categorize the non-linearity between the features, we propose the kernalized discriminant analysis concept into the score prediction metric. Our proposed similarity measure method is Discriminant Kernalized Disparity (DKD) metric. In DKD, kernel applied Eigen decomposition is utilized for each feature set and disparity metric is evaluated. Finally the proposed system is simulated and evaluated with different medical database images and retrieval performance of Retrieval Efficiency, Normalized Average Rank of Retrieval, Precision, Recall, SSIM and processing Time. Some previous implementations are examined and compared to our results. The proposed method has produced Retrieval efficiency of 98.4%, NARR of 15.18%, Precision and Recall of 98.4%, F1 score of 98.39%, Correlation of 98.2%, SSIM of 97.92% and all at a speed of 30 s 5% more precision as well as less processing time are achieved compared to prior efforts by our proposed solution.

Medical image retrieval using a novel local relative directional edge pattern and Zernike moments

Medical image retrieval using a novel local relative directional edge pattern and Zernike moments

An Accurate and Privacy-Preserving Retrieval Scheme Over Outsourced Medical Images

With the rapid advancement in medical imaging techniques, Content-Based (medical) Image Retrieval (CBIR), which can assist in disease diagnosis, has gained much attention in both academia and industry. However, due to patients’ sensitive information involved in medical images, privacy-preserving CBIR is a challenge worth exploiting. Though several privacy-preserving CBIR schemes have been put forth, they can only resist known-background attack (KBA), and do not suffice for protecting the image privacy in outsourced settings. In this article, aiming at the above challenge, we first design a novel Privacy-preserving Mahalanobis Distance Comparison (PMDC) method to improve the accuracy of medical images retrieval. Then, combined with the Mahalanobis distance based Fuzzy C-Means (FCM-M) algorithm, a scheme named TAMMIE is proposed to achieve accurate and privacy-preserving medical image retrieval over encrypted data. With TAMMIE, an image owner can securely outsource the images and indexes to a cloud server, and query users can request retrieval services from the cloud server while keeping their queries private. Detailed security analysis shows that our proposed schemes are secure under the attack stronger than KBA. Furthermore, thorough empirical experiments conducted on two real-world and one synthetic datasets also demonstrate the efficiency of TAMMIE.

Multi-scale Triplet Hashing for Medical Image Retrieval

For medical image retrieval task, deep hashing algorithms are widely applied in large-scale datasets for auxiliary diagnosis due to the retrieval efficiency advantage of hash codes. Most of which focus on features learning, whilst neglecting the discriminate area of medical images and hierarchical similarity for deep features and hash codes. In this paper, we tackle these dilemmas with a new Multi-scale Triplet Hashing (MTH) algorithm, which can leverage multi-scale information, convolutional self-attention and hierarchical similarity to learn effective hash codes simultaneously. The MTH algorithm first designs multi-scale DenseBlock module to learn multi-scale information of medical images. Meanwhile, a convolutional self-attention mechanism is developed to perform information interaction of the channel domain, which can capture the discriminate area of medical images effectively. On top of the two paths, a novel loss function is proposed to not only conserve the category-level information of deep features and the semantic information of hash codes in the learning process, but also capture the hierarchical similarity for deep features and hash codes. Extensive experiments on the Curated X-ray Dataset, Skin Cancer MNIST Dataset and COVID-19 Radiography Dataset illustrate that the MTH algorithm can further enhance the effect of medical retrieval compared to other state-of-the-art medical image retrieval algorithms.

Interpretable features fusion with precision MRI images deep hashing for brain tumor detection

Background and ObjectiveBrain tumor is a deadly disease that can affect people of all ages. Radiologists play a critical role in the early diagnosis and treatment of the 14,000 persons diagnosed with brain tumors on average each year. The best method for tumor detection with computer-aided diagnosis systems (CADs) is Magnetic Resonance Imaging (MRI). However, manual evaluation using conventional approaches may result in a number of inaccuracies due to the complicated tissue properties of a large number of images. Therefore a precision medical image hashing approach is proposed that combines interpretability and feature fusion using MRI images of brain tumors, to address the issue of medical image retrieval. MethodsA precision hashing method combining interpretability and feature fusion is proposed to recover the problem of low image resolutions in brain tumor detection on the Brain-Tumor-MRI (BT-MRI) dataset. First, the dataset is pre-trained with the DenseNet201 network using the Comparison-to-Learn method. Then, a global network is created that generates the salience map to yield a mask crop with local region discrimination. Finally, the local network features inputs and public features expressing the local discriminant regions are concatenated for the pooling layer. A hash layer is added between the fully connected layer and the classification layer of the backbone network to generate high-quality hash codes. The final result is obtained by calculating the hash codes with the similarity metric. ResultsExperimental results with the BT-MRI dataset showed that the proposed method can effectively identify tumor regions and more accurate hash codes can be generated by using the three loss functions in feature fusion. It has been demonstrated that the accuracy of medical image retrieval is effectively increased when our method is compared with existing image retrieval approaches. ConclusionsOur method has demonstrated that the accuracy of medical image retrieval can be effectively increased and potentially applied to CADs.

A computational model for medical image retrieval using orthogonal moment

This Paper Deals With Image Retrieval Process Of Liver Computer Tomography (Ct) Scan Images Using Orthogonal Moment Features And Content Based Image Retrieval. Medical Images Are Useful Diagnostic Evidence As It Can Provide Vital Information In Anatomical Pathology. The Objective Is To Efficiently Retrieve Medical Images From The Database Using Orthogonal Moments And Content Based Image Retrieval Methods. The Orthogonal Moment Viz Discrete Racah Polynomial, Continuous Legendre Moments And Zernike Moments Are Computed For The Study. The Region Of Interest Based Segmentation And Watershed Segmentation Is Applied To The Preprocessed Input Images And Features Are Extracted Using Orthogonal Moments And Shape And Texture Features Are Extracted Using Content Based Image Retrieval (Cbir). The Performances Of Each Moment In Terms Of Accuracy And Error Rate Are Compared With Cbir.

RbQE: An Efficient Method for Content-Based Medical Image Retrieval Based on Query Expansion

Systems for retrieving and managing content-based medical images are becoming more important, especially as medical imaging technology advances and the medical image database grows. In addition, these systems can also use medical images to better grasp and gain a deeper understanding of the causes and treatments of different diseases, not just for diagnostic purposes. For achieving all these purposes, there is a critical need for an efficient and accurate content-based medical image retrieval (CBMIR) method. This paper proposes an efficient method (RbQE) for the retrieval of computed tomography (CT) and magnetic resonance (MR) images. RbQE is based on expanding the features of querying and exploiting the pre-trained learning models AlexNet and VGG-19 to extract compact, deep, and high-level features from medical images. There are two searching procedures in RbQE: a rapid search and a final search. In the rapid search, the original query is expanded by retrieving the top-ranked images from each class and is used to reformulate the query by calculating the mean values for deep features of the top-ranked images, resulting in a new query for each class. In the final search, the new query that is most similar to the original query will be used for retrieval from the database. The performance of the proposed method has been compared to state-of-the-art methods on four publicly available standard databases, namely, TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI. Experimental results show that the proposed method exceeds the compared methods by 0.84%, 4.86%, 1.24%, and 14.34% in average retrieval precision (ARP) for the TCIA-CT, EXACT09-CT, NEMA-CT, and OASIS-MRI databases, respectively.

Federated Learning to Safeguard Patients Data: A Medical Image Retrieval Case

Healthcare data are distributed and confidential, making it difficult to use centralized automatic diagnostic techniques. For example, different hospitals hold the electronic health records (EHRs) of different patient populations; however, transferring this data between hospitals is difficult due to the sensitive nature of the information. This presents a significant obstacle to the development of efficient and generalizable analytical methods that require a large amount of diverse Big Data. Federated learning allows multiple institutions to work together to develop a machine learning algorithm without sharing their data. We conducted a systematic study to analyze the current state of FL in the healthcare industry and explore both the limitations of this technology and its potential. Organizations share the parameters of their models with each other. This allows them to reap the benefits of a model developed with a richer data set while protecting the confidentiality of their data. Standard methods for large-scale machine learning, distributed optimization, and privacy-friendly data analytics need to be fundamentally rethought to address the new problems posed by training on diverse networks that may contain large amounts of data. In this article, we discuss the particular qualities and difficulties of federated learning, provide a comprehensive overview of current approaches, and outline several directions for future work that are relevant to a variety of research communities. These issues are important to many different research communities.

Centralized contrastive loss with weakly supervised progressive feature extraction for fine-grained common thorax disease retrieval in chest x-ray.

Medical images have already become an essential tool for the diagnosis of many diseases. Thus a large number of medical images are being generated due to the daily routine inspection. An efficient image-based disease retrieval system will not only make full use of existing data, but also help physicians to prognosis the diseases. Medical image retrieval is represented by the classification and localization of common thorax diseases in x-ray images. Although extensive efforts have been put into this field, there are still manychallenges. Most of the existing fine-grained image research methods just apply existing deep learning frameworks in extracting the image features. However, these high-level features mainly focus on the global representations of the object, rather than simultaneously considering the local ones. It requires fine-grained details to classify the images with similar lesion areas. Thus, it is necessary to combine the global features and local ones to make the features more discriminative. On the other hand, training CNN models based on current existing strategies have a high time complexity, and is hard to get the discriminative features mentioned above. In addition, the visual retrieval method of fine-grained medical images still has the problem of insufficient sample data with accurate annotationinformation. To address above challenges, we introduced a novel fine-grained medical images retrieval method. First, a centralized contrastive loss (CCLoss) is proposed as our metric learning loss function. Parameters are updated by using the center point, which not only improves the distinguishing performance of features, but also effectively reduces the time complexity of the algorithm. In addition, a weakly supervised progressive feature extraction method is proposed to gradually extract the combined features. And the attention mechanism module is applied to screen the target information after the initial positioning for fine refinement, so as to separate the features with a high degree of discrimination. The retrieval of 14 different chest diseases is evaluated on the chest x-raydatasets. Compared with the existing research methods, the proposed method shows a better retrieval result for Recall@8 by 2.26 and achieves a very efficient training speed which is 100 times faster than the pair-wise loss-based training strategy. We also assessed the effects of Recall@k (k = 2, 4, 6, 8) for progressive features extracted from different steps to obtain a model with the best retrievalperformance. The proposed model is capable of learning discriminative representations from chest x-ray datasets, and it achieves better performance compared with other state-of-the-art methods. Therefore, the developed model would be useful in the diagnosis of common thorax disease or unknown chestdisease.

Power-Efficient VLSI Architecture of a New Class of Dyadic Gabor Wavelets for Medical Image Retrieval

Gabor wavelet is widely used in the analysis of texture features. It is found that Gabor wavelet filter bank (FB) requires infinite precision (due to irrational coefficients) to extract accurate directional textural features for which it needs numerous computations and external memory access. Due to this, there is a requirement for huge digital hardware, increased dynamic power dissipation, and increased processing time. In order to solve these issues, this article presents the first dyadic Gabor wavelet FB (DGWFB) based on the slight alteration in orientation parameter without disturbing the remaining Gabor wavelet parameters. In addition, this article introduces a separable VLSI architecture for the proposed DGWFB. The proposed VLSI architecture is implemented in field-programmable gate array (FPGA) through Xilinx Kintex 7 platform. It is observed that the proposed DGWFB has reduced processing time, dynamic power dissipation and digital hardware requirement significantly. The effectiveness of the designed DGWFB is evaluated in medical image retrieval application using three different publicly available medical databases namely NEMA, OASIS, and EXACT09. The proposed DGWFB achieved better performance as compared to existing Gabor wavelet FBs with significantly reduced digital hardware and processing time.

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

A dynamic programming approach for accurate content-based retrieval of ordinary and nano-scale medical images

Convoluted Neighborhood-Based Ordered-Dither Block Truncation Coding for Ear Image Retrieval

Image retrieval is a significant and hot research topic among researchers that drives the focus of researchers from keyword toward semantic-based image reconstruction. Nevertheless, existing image retrieval investigations still have a shortage of significant semantic image definition and user behavior consideration. Hence, there is a necessity to offer a high level of assistance towards regulating the semantic gap between low-level visual patterns and high-level ideas for a better understanding between humans and machines. Hence, this research devises an effective medical image retrieval strategy using convoluted neighborhood-based Ordered-dither block truncation coding (ODBTC). The developed approach is devised by modifying the ODBTC concept using a convoluted neighborhood mechanism. Here, the convoluted neighborhood-based color co-occurrence feature (CCF) and convoluted neighborhood-based bit pattern feature (BBF) are extracted. Finally, cross-indexing is performed to convert the feature points into binary codes for effective image retrieval. Meanwhile, the proposed convoluted neighborhood-based ODBTC has achieved maximum precision, recall, and f-measure with values of 0.740, 0.680, and 0.709.

Computer-aided diagnosis through medical image retrieval in radiology

Currently, radiologists face an excessive workload, which leads to high levels of fatigue, and consequently, to undesired diagnosis mistakes. Decision support systems can be used to prioritize and help radiologists making quicker decisions. In this sense, medical content-based image retrieval systems can be of extreme utility by providing well-curated similar examples. Nonetheless, most medical content-based image retrieval systems work by finding the most similar image, which is not equivalent to finding the most similar image in terms of disease and its severity. Here, we propose an interpretability-driven and an attention-driven medical image retrieval system. We conducted experiments in a large and publicly available dataset of chest radiographs with structured labels derived from free-text radiology reports (MIMIC-CXR-JPG). We evaluated the methods on two common conditions: pleural effusion and (potential) pneumonia. As ground-truth to perform the evaluation, query/test and catalogue images were classified and ordered by an experienced board-certified radiologist. For a profound and complete evaluation, additional radiologists also provided their rankings, which allowed us to infer inter-rater variability, and yield qualitative performance levels. Based on our ground-truth ranking, we also quantitatively evaluated the proposed approaches by computing the normalized Discounted Cumulative Gain (nDCG). We found that the Interpretability-guided approach outperforms the other state-of-the-art approaches and shows the best agreement with the most experienced radiologist. Furthermore, its performance lies within the observed inter-rater variability.

Pre-trained convolution neural networks models for content-based medical image retrieval

Content-based image retrieval (CBIR) is a recent method used to retrieve different types of images from repositories. The traditional content-based medical image retrieval (CBMIR) methods commonly used low-level image representation features extracted from color, texture, and shape image descriptors. Since most of these CBMIR systems depend mainly on the extracted features, the methods used in the feature extraction phase are more important. Features extraction methods, which generate inaccurate features, lead to very poor performance retrieval because of semantic gap widening. Hence, there is high demand for independent domain knowledge features extraction methods, which have automatic learning capabilities from input images. Pre-trained deep convolution neural networks (CNNs), the recent generation of deep learning neural networks, could be used to extract expressive and accurate features. The main advantage of these pre-trained CNNs models is the pre-training process for huge image data of thousands of different classes, and their knowledge after the training process could easily be transferred. There are many successful models of pre-trained CNNs models used in the area of medical image retrieval, image classification, and object recognition. This study utilizes two of the most known pre-trained CNNs models; ResNet18 and SqueezeNet for the offline feature extraction stage. Additionally, the highly accurate features extracted from medical images are used for the CBMIR method of medical image retrieval. This study uses two popular medical image datasets; Kvasir and PH2 to show that the proposed methods have good retrieval results. The retrieval performance evaluation measures of our proposed method have average precision of 97.75% and 83.33% for Kvasir and PH2 medical images respectively, and outperform some of the state-of-the-art methods in this field of study because these pre-trained CNNs have well trained layers among a huge number of image types. Finally, intensive statistical analysis shows that the proposed ResNet18-based retrieval method has the best performance for enhancing both recall and precision measures for both medical images.

A comprehensive review of content-based image retrieval systems using deep learning and hand-crafted features in medical imaging: Research challenges and future directions

Computer-based medical image retrieval (CBMIR) system helps practitioners to enhance their diagnostic abilities, speeds up accurate diagnosis, and minimizes intra-and inter-observer variability. We investigate in-depth insights on content-based image retrieval systems over the past 20 years, to enable improvements with an imperative feature set to enhance the performance of the proposed CBMIR. After a comprehensive survey, we have made some inferences from the literature survey which is beneficial for researchers and medical practitioners. Existing studies, consider patch sizes of 32 only where 16 and 64 are ignored. We conduct experiments using 16, 32, and 64 patch sizes and two retrieval approaches i.e. simple distance-based retrieval (SDR) and customized query-based approach (CQA). SDR exhibits high inter-class ambiguity, in which some of the retrieved images are from different classes posing similar feature values, which negatively impacts the retrieval performance. To tackle this, a framework has been proposed based on CQA-CBMIR which utilizes wavelet-based Riesz features and improves the retrieval outcomes. The proposed framework is compared with the state-of-the-art (SOTA) methods and obtained f1-score and accuracy i.e. 86.5% and 86.8% respectively. Furthermore, this study illustrates some of the unresolved issues in the literature that demand future researchers' utmost attention to investigate new pathways in this discipline.