Transfer Learning-based Model Research Articles

Inverted bell-curve-based ensemble of deep learning models for detection of COVID-19 from chest X-rays

Novel Coronavirus 2019 disease or COVID-19 is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The use of chest X-rays (CXRs) has become an important practice to assist in the diagnosis of COVID-19 as they can be used to detect the abnormalities developed in the infected patients’ lungs. With the fast spread of the disease, many researchers across the world are striving to use several deep learning-based systems to identify the COVID-19 from such CXR images. To this end, we propose an inverted bell-curve-based ensemble of deep learning models for the detection of COVID-19 from CXR images. We first use a selection of models pretrained on ImageNet dataset and use the concept of transfer learning to retrain them with CXR datasets. Then the trained models are combined with the proposed inverted bell curve weighted ensemble method, where the output of each classifier is assigned a weight, and the final prediction is done by performing a weighted average of those outputs. We evaluate the proposed method on two publicly available datasets: the COVID-19 Radiography Database and the IEEE COVID Chest X-ray Dataset. The accuracy, F1 score and the AUC ROC achieved by the proposed method are 99.66%, 99.75% and 99.99%, respectively, in the first dataset, and, 99.84%, 99.81% and 99.99%, respectively, in the other dataset. Experimental results ensure that the use of transfer learning-based models and their combination using the proposed ensemble method result in improved predictions of COVID-19 in CXRs.

HSI-LFS-BERT: Novel Hybrid Swarm Intelligence Based Linguistics Feature Selection and Computational Intelligent Model for Alzheimer’s Prediction Using Audio Transcript

Alzheimer’s dementia (AD) affects memory, language, and cognition which worsens over time. It is critical to develop a reliable early detection method before permanent brain atrophy and cognitive impairment. This study uses clinical transcripts, a text-based adaptation of the original audio recordings of Alzheimer’s patients. This audio transcript data is taken from DementiaBank which is the largest public dataset of AD transcripts. The study aims to show how transfer learning-based models and swarm intelligence optimization techniques can be used to predict Alzheimer’s disease. To enhance the prediction performance for Alzheimer’s disease, a hybrid swarm intelligence linguistic feature selection (HSI-LFS) approach is proposed that extracts a combined feature set using Particle Swarm Optimization (PSO), Dragonfly Optimization (DO), and Grey Wolf Optimization (GWO) algorithms. In addition, a transfer learning-based model called HSI-LFS-BERT, a combination of the HSI-LFS feature selection method and Bidirectional Encoder Representations from Transformer (BERT) algorithm is proposed. The proposed model is compared using two feature sets: first set consists of the initial feature set and the second set contains hybrid feature set that has been extracted using the suggested HSI-LFS method. The BERT embedding with HSI-LFS outperformed the conventional feature set providing the most accurate modeling parameters while reducing the computations by 27.19%. The proposed HSI-LFS-BERT model excelled state-of-the-art models achieving 98.24% accuracy, 91.56% precision, and 98.78% recall.

Cross-Lingual Few-Shot Hate Speech and Offensive Language Detection Using Meta Learning

Automatic detection of abusive online content such as hate speech, offensive language, threats, etc. has become prevalent in social media, with multiple efforts dedicated to detecting this phenomenon in English. However, detecting hatred and abuse in low-resource languages is a non-trivial challenge. The lack of sufficient labeled data in low-resource languages and inconsistent generalization ability of transformer-based multilingual pre-trained language models for typologically diverse languages make these models inefficient in some cases. We propose a meta learning-based approach to study the problem of few-shot hate speech and offensive language detection in low-resource languages that will allow hateful or offensive content to be predicted by only observing a few labeled data items in a specific target language. We investigate the feasibility of applying a meta learning approach in cross-lingual few-shot hate speech detection by leveraging two meta learning models based on optimization-based and metric-based (MAML and Proto-MAML) methods. To the best of our knowledge, this is the first effort of this kind. To evaluate the performance of our approach, we consider hate speech and offensive language detection as two separate tasks and make two diverse collections of different publicly available datasets comprising 15 datasets across 8 languages for hate speech and 6 datasets across 6 languages for offensive language. Our experiments show that meta learning-based models outperform transfer learning-based models in a majority of cases, and that Proto-MAML is the best performing model, as it can quickly generalize and adapt to new languages with only a few labeled data points (generally, 16 samples per class yields an effective performance) to identify hateful or offensive content.

A Novel COVID-19 Detection Model Based on DCGAN and Deep Transfer Learning

A continuing outbreak of pneumonia-related disease novel, Coronavirus has been recorded worldwide and has become a global health problem. This research aims to generate a constructive training data set for a neural network to detect COVID-19 from X-ray images. The creation of medical images is an issue in the field of deep learning. Medical image datasets are frequently unbalanced; using such datasets to train a deep neural network model to correctly classify medical conditions typically leads to over-fitting the data on majority class samples. Data augmentation is commonly used in training data to expand the dataset. Data augmentation may not be beneficial in medical domains with limited data. This paper proposed a data generation model using a Deep Convolutional Generative adversarial network (DCGAN), which generates fake instances with comparable properties to the original data. The model's Fréchet Distance of Inception (FID) was 23.78, close to the original data. Deep transfer learning-based models VGG-16, Inceptionv3 and MobilNet, were chosen as the backbone for COVID-19 detection. The present study aims to increase the dataset using the DCGAN data augmentation technique to improve classifier performance.

Improving Effectiveness of Different Deep Transfer Learning-Based Models for Detecting Brain Tumors From MR Images

Early classification of brain tumors from magnetic resonance imaging (MRI) plays an important role in the diagnosis of such diseases. There are many diagnostic imaging methods used to identify tumors in the brain. MRI is commonly used for such tasks because of its unmatched image quality. The relevance of artificial intelligence (AI) in the form of deep learning (DL) has revolutionized new methods of automated medical image diagnosis. This study aimed to develop a robust and efficient method based on transfer learning technique for classifying brain tumors using MRI. In this article, the popular deep learning architectures are utilized to develop brain tumor diagnostic system. The pre-trained models such as Xception, NasNet Large, DenseNet121 and InceptionResNetV2 are used to extract the deep features from brain MRI. The experiment was performed using two benchmark datasets that are openly accessible from the web. Images from the dataset were first cropped, preprocessed, and augmented for accurate and fast training. Deep transfer learning models are trained and tested on a brain MRI dataset using three different optimization algorithms (ADAM, SGD, and RMSprop). The performance of the transfer learning models is evaluated using performance metrics such as accuracy, sensitivity, precision, specificity and F1-score. From the experimental results, our proposed CNN model based on the Xception architecture using ADAM optimizer is better than the other three proposed models. The Xception model achieved accuracy, sensitivity, precision specificity, and F1-score values of 99.67%, 99.68%, 99.68%, 99.66%, and 99.68% on the MRI-large dataset, and 91.94%, 96.55%, 87.50%, 87.88%, and 91.80% on the MRI-small dataset, respectively. The proposed method is superior to the existing literature, indicating that it can be used to quickly and accurately classify brain tumors.

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy.

This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in patients with type 1 diabetes (T1D). The Kaggle dataset, which is a publicly available dataset, was divided into training and testing Kaggle datasets. In the comparison dataset, we collected retinal fundus images of T1D patients at Chang Gung Memorial Hospital in Taiwan from 2013 to 2020, and the images were divided into training and testing T1D datasets. The model was developed using 4 different convolutional neural networks (Inception-V3, DenseNet-121, VGG1, and Xception). The model performance in predicting DR was evaluated using testing images from each dataset, and area under the curve (AUC), sensitivity, and specificity were calculated. The model trained using the Kaggle dataset had an average (range) AUC of 0.74 (0.03) and 0.87 (0.01) in the testing Kaggle and T1D datasets, respectively. The model trained using the T1D dataset had an AUC of 0.88 (0.03), which decreased to 0.57 (0.02) in the testing Kaggle dataset. Heatmaps showed that the model focused on retinal hemorrhage, vessels, and exudation to predict DR. In wrong prediction images, artifacts and low-image quality affected model performance. The model developed with the high variability and T2D predominant dataset could be applied to T1D patients. Dataset homogeneity could affect the performance, trainability, and generalization of the model.

Transfer learning augmented enhanced memory network models for reference evapotranspiration estimation

Accurate estimation of reference evapotranspiration is a prerequisite for achieving sustainable development, designing irrigation plans, and efficient utilization of water resources. Traditionally, several conventional and statistical methods have been developed for this time-series prediction task. However, these methods are not well capable of handling the non-linear complexities, historical dependencies of the data and are majorly external features driven. In recent years, machine learning and deep learning models have gained significant attention for achieving state-of-the-art results in nearly all engineering domains. In this context, the present research study proposes three deep learning-based models to estimate future evapotranspiration values. Initially, a moving window-based baseline model is proposed for prediction by utilizing the minimum available historic evapotranspiration data only. The proposed model provides support for handling historical data dependencies by employing the Long Short Term Memory Network (LSTMN) model. Then, the concept of transfer learning is introduced/augmented for improving the prediction performance of the baseline model. Two transfer learning augmented deep learning prediction models (TL−LSTM1 and TL−LSTM2) are developed to forecast the future reference evapotranspiration values. The application of the proposed three models (Baseline, TL−LSTM1 and TL−LSTM2) is demonstrated on the dataset of nine different locations of State Punjab, India. The performance of the proposed models is compared with the prediction results of three supervised benchmark models in terms of well-known performance measures. The overall prediction results indicate that the transfer learning-based models achieve promising results.

Deep vision-based surveillance system to prevent train–elephant collisions

Animal conservation is imperative, and technology can certainly assist in different ways. The extinction of endangered species like tigers and elephants has boosted the necessity for such efforts. Human–elephant collision (HEC) has been an active area of research for years. Apart from deforestation, the roads and rail tracks laid down through forest areas intervene a lot in wildlife. Collisions and tragedies are every day, especially in green belts in India and other Asian countries. Therefore, it is crucial to develop vision-based, automated, warning-generating systems to identify the animal/elephant near-site. In the proposed work, different deep learning-based models are proposed to identify elephants in image/video. Several convolutional neural network (CNN)-based models and three transfer learning (TL)-based models, i.e., ResNet50, MobileNet, Inception V3, have been experimented with and tuned for elephant detection. All the models are tested on a synthesized dataset having about 4200 images built using two public datasets, i.e., ELPephant and RailSem19. Two accurate CNN and transfer learning-based models are presented in detail. These highly accurate and precise models can alarm the trains and generate warning signals on site. The proposed CNN and inception network demonstrated high accuracy of 99.53% and 99.91%, respectively, and are remarkable in identifying elephants and hence preventing HEC. The same model can be trained for other animals for their preservation in similar scenarios.

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause pneumonia. The aim of this paper is to develop a rapid and accurate medical diagnosis support system to detect COVID-19 in chest X-ray images using a stacking approach combining transfer learning techniques and KNN algorithm for selection of the best model. In deep learning, we have multiple approaches for building a classification system for analyzing radiographic images. In this work, we used the transfer learning technique. This approach makes it possible to store and use the knowledge acquired from a pretrained convolutional neural network to solve a new problem. To ensure the robustness of the proposed system for diagnosing patients with COVID-19 using X-ray images, we used a machine learning method called the stacking approach to combine the performances of the many transfer learning-based models. The generated model was trained on a dataset containing four classes, namely, COVID-19, tuberculosis, viral pneumonia, and normal cases. The dataset used was collected from a six-source dataset of X-ray images. To evaluate the performance of the proposed system, we used different common evaluation measures. Our proposed system achieves an extremely good accuracy of 99.23% exceeding many previous related studies.

Deep Transfer Learning-Based COVID-19 Prediction Using Chest X-Rays

The novel coronavirus disease (COVID-19) is spreading very rapidly across the globe because of its highly contagious nature and is declared as a pandemic by the World Health Organization (WHO). Scientists are endeavouring to ascertain the drugs for its efficacious treatment. Because, until now, no full-proof drug is available to cure this deadly disease. Therefore, identifying COVID-19 positive people and quarantining them can be an effective solution to control its spread. Many machine learning and deep learning techniques are being used quite effectively to classify positive and negative cases. In this work, a deep transfer learning-based model is proposed to classify the COVID-19 cases using chest X-rays or CT scan images of infected persons. The proposed model is based on the ensembling of DenseNet121 and SqueezeNet1.0, which is named as DeQueezeNet. The model can extract the importance of various influential features from the X-ray images, which are effectively used to classify the COVID-19 cases. The performance study of the proposed model depicts its effectiveness in terms of accuracy and precision. A comparative study has also been done with the recently published works, and it is observed that the performance of the proposed model is significantly better.

A deep transfer learning based model for automatic detection of COVID-19 from chest X-ray

Deep learning in medical imaging has revolutionized the way we interpret medical data, as high computational devices' capabilities are far more than their creators. With the pandemic causing havoc for the second straight year, the findings in our paper will allow researchers worldwide to use and create state-of-the-art models to detect affected persons before it reaches the R number. The paper proposes an automated diagnostic tool using the deep learning models on chest x-rays as an input to reach a point where we surpass this pandemic (COVID-19 disease). A deep transfer learning-based model for automatic detection of COVID-19 from chest x-rays using the Inception-V3 model is proposed, in which we added flattening, node dropping, normalization, and dense layer. The proposed architecture is compared with existing state-of-the-art ImageNet models. The model?s efficacy is tested on three different COVID-19 radiography datasets with three classes: COVID, normal, and viral pneumonia. The proposed model has reached an accuracy of 97.7%, 84.95%, and 97.03% on the mentioned datasets, respectively. The proposed work introduces the deep neural networks applied to medical images to analyze image enhancement techniques and emphasize the field's clinical aspects.

Deep learning-based prediction of the T cell receptor-antigen binding specificity.

Neoantigens play a key role in the recognition of tumor cells by T cells. However, only a small proportion of neoantigens truly elicit T cell responses, and fewer clues exist as to which neoantigens are recognized by which T cell receptors (TCRs). We built a transfer learning-based model, named pMHC-TCR binding prediction network (pMTnet), to predict TCR-binding specificities of neoantigens, and T cell antigens in general, presented by class I major histocompatibility complexes (pMHCs). pMTnet was comprehensively validated by a series of analyses, and showed advance over previous work by a large margin. By applying pMTnet in human tumor genomics data, we discovered that neoantigens were generally more immunogenic than self-antigens, but HERV-E, a special type of self-antigen that is re-activated in kidney cancer, is more immunogenic than neoantigens. We further discovered that patients with more clonally expanded T cells exhibiting better affinity against truncal, rather than subclonal, neoantigens, had more favorable prognosis and treatment response to immunotherapy, in melanoma and lung cancer but not in kidney cancer. Predicting TCR-neoantigen/antigen pairs is one of the most daunting challenges in modern immunology. However, we achieved an accurate prediction of the pairing only using the TCR sequence (CDR3β), antigen sequence, and class I MHC allele, and our work revealed unique insights into the interactions of TCRs and pMHCs in human tumors using pMTnet as a discovery tool.

Transfer learning based robust automatic detection system for diabetic retinopathy grading

Diabetic retinopathy (DR) can be categorized on the basis of prolonged complication in the retinal blood vessels which may lead to severe blindness. Early stage prediction and diagnosis of DR requires regular eye examination to reduce the complications causing vision loss. Indicative significance of DR forecast and evaluation to help the ophthalmologists in standard screening has prompted the improvement of computerized DR recognition frameworks. This work focuses on automatic DR disease identification and its grading by the means of transfer learning approach using dynamic investigation. Our proposed approach utilizes deep neural network for feature extraction from fundus images and these features are further ensembled with supervised machine learning technique for DR grading. An optimized classification is achieved by applying an ensemble of convolution neural networks (CNNs) with statistical feature selection module and SVM classifier. The learning of classifier is achieved by the feature information transferred from CNN model to the SVM classifier, which results in remarkable performance of the learned models. Statistically optimized feature set utilized for transfer learning technique yields in the classification accuracy of 90.51% with proposed Prominent Feature-based Transfer Learning (PFTL) method employing Inception V3 model. The cost analysis of the proposed model provides a minimum cross-entropy loss of 0.295 consuming the time of 38 min 53 s, thus, maintaining a trade-off. The generalization ability of the proposed model is established by the performance assessment using latest IDRiD dataset that yields accuracy of 90.01% for Inception V3 network providing uniform outcomes for all the evaluation parameters. The diagnosis ability of the proposed transfer learning-based model is justified by comparing the proposed methods with the state-of-the-art methods. The optimized PFTL model (CNN + Statistical Analysis + SVM) outperforms other classification algorithms and provides the maximum accuracy improvement of 16.01% over the state-of-the-art techniques.

Identification of Civil Infrastructure Damage Using Ensemble Transfer Learning Model

This article uses cutting‐edge deep learning technology to identify structural damage from images for a civil engineering application. The public infrastructures of the country are generally inspected physically by a visual evaluation by qualified inspectors. However, manual inspections are pretty time‐consuming and often require too much labor. The number of experts capable of evaluating such structural damage is inadequate. As a result, computer vision‐based techniques for automatic damage detection have been developed. This paper’s civil infrastructure damages are classified into four damages of roads common in Indian highways and the concrete deterioration in the bridges. The convolutional neural network has become a standard tool for organizing and recognizing images. In this paper, an ensemble of three CNN models is proposed, and two are transfer learning‐based models. The proposed ensemble transfer learning model provided a validation accuracy of 87.1%.

Prognostic Value of Transfer Learning Based Features in Resectable Pancreatic Ductal Adenocarcinoma.

Background: Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most aggressive cancers with an extremely poor prognosis. Radiomics has shown prognostic ability in multiple types of cancer including PDAC. However, the prognostic value of traditional radiomics pipelines, which are based on hand-crafted radiomic features alone is limited.Methods: Convolutional neural networks (CNNs) have been shown to outperform radiomics models in computer vision tasks. However, training a CNN from scratch requires a large sample size which is not feasible in most medical imaging studies. As an alternative solution, CNN-based transfer learning models have shown the potential for achieving reasonable performance using small datasets. In this work, we developed and validated a CNN-based transfer learning model for prognostication of overall survival in PDAC patients using two independent resectable PDAC cohorts.Results: The proposed transfer learning-based prognostication model for overall survival achieved the area under the receiver operating characteristic curve of 0.81 on the test cohort, which was significantly higher than that of the traditional radiomics model (0.54). To further assess the prognostic value of the models, the predicted probabilities of death generated from the two models were used as risk scores in a univariate Cox Proportional Hazard model and while the risk score from the traditional radiomics model was not associated with overall survival, the proposed transfer learning-based risk score had significant prognostic value with hazard ratio of 1.86 (95% Confidence Interval: 1.15–3.53, p-value: 0.04).Conclusions: This result suggests that transfer learning-based models may significantly improve prognostic performance in typical small sample size medical imaging studies.

Deep transfer learning-based automated detection of COVID-19 from lung CT scan slices.

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.

Automated identification of cataract severity using retinal fundus images

ABSTRACT Cataract is the most prevalent cause of blindness worldwide, which accounts for more than 51% of overall blindness. The early detection of cataract can salvage impaired vision leading to blindness. Most of the existing cataract classification systems are based on traditional machine learning methods with hand-engineered features. The manual extraction of retinal features is generally a time-taking process and requires professional ophthalmologists. Convolutional neural network (CNN) is a widely accepted model for automatic feature extraction, but it necessitates a larger dataset to evade overfitting problems. Contrarily, classification using SVM has great generalisation power to elucidate small-sample dataset. Therefore, we proposed a hybrid model by integrating deep learning models and SVM for 4-class cataract classification. The transfer learning-based models (AlexNet, VGGNet, ResNet) are employed for automatic feature extraction and SVM performs as a recogniser. The proposed architecture evaluated on 8030 retinal images with strong feature extraction and classification techniques has achieved 95.65% of accuracy. The results of this study have verified that the proposed method outperforms conventional methods and can provide a reference for other retinal diseases.

Abstract 2098: AzinNet: A wavelet convolutional neural network for pathology image analysis

Abstract Deep learning models are gaining popularity for pathology image analysis in cancer and have successfully been used to build disease and mutation status prediction models [1,2]. However, there are several shortcomings to these models. They typically tile whole slide images with little or no pre-processing leaving small artifacts, such as stitching artifacts, intact in the training data. The network may correlate these artifacts with class labels instead of, or in addition to, biologically meaningful morphological features. While one may argue transfer learning can be used to guard against such artifacts, (1) to the best of our knowledge no rigorous study has been performed to validate this idea given whole slide images, and (2) transfer learning-based deep learning models, such as the one studied in [2], show sensitivity to small artifacts in the data. That being said, transfer learning based methods usually borrow from models trained on the image-net dataset, in which each image contains one object we wish to classify. In contrast, pathology images contain morphological features at different scales spread through the slide, which may be best understood as texture-type features. This begs the question of whether transfer learning models, when used as black-box universal feature extractors, miss important morphological features due to the different nature of features encountered in pathology images. To address these issues we propose a wavelet based convolutional neural network, called AzinNet, inspired by the wavelet based network of [3]. AzinNet uses wavelet transform to (1) denoise the data and remove small artifacts, and (2) create a hierarchy of wavelet channels representing morphological features at different scales. We train and test AzinNet on TCGA whole slide images, and compare it with transfer learning based models inputted with raw and denoised images. We also compare robustness of AzinNet and transfer learning-based models to small perturbations of the data. Initial results suggest AzinNet achieves a higher area under curve and is more robust to data perturbations.

Transfer Learning-Based Framework for Classification of Pest in Tomato Plants

ABSTRACT Pest in the plant is a major challenge in the agriculture sector. Hence, early and accurate detection and classification of pests could help in precautionary measures while substantially reducing economic losses. Recent developments in deep convolutional neural network (CNN) have drastically improved the accuracy of image recognition systems. In this paper, we have presented a transfer learning of pre-trained deep CNN-based framework for classification of pest in tomato plants. The dataset for this study has been collected from online sources that consist of 859 images categorized into 10 classes. This study is first of its kind where: (i) dataset with 10 classes of tomato pest are involved; (ii) an exhaustive comparison of the performance of 15 pre-trained deep CNN models has been presented on tomato pest classification. The experimental results show that the highest classification accuracy of 88.83% has been obtained using DenseNet169 model. Further, the encouraging results of transfer learning-based models demonstrate its effectiveness in pest detection and classification tasks.

Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features

Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods.