An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Habib, Sami J.; Alsanea, Majed; Aloraini, Mohammed; Al-Rawashdeh, Hazim Saleh; Islam, Muhammad; Khan, Sheroz

doi:10.3390/s22072602

Cited by 33 publications

(21 citation statements)

References 46 publications

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“…In this research, we explored vision-based Transformer architecture for SC classification as shown in Figure 2 , where the MVT architecture receives an image as input data with a size of 72 by 72. First, the input image is converted into patches as in [ 55 ]. MVT supports a different number of patches based on the underline scenario.…”

Section: The Proposed Methodsmentioning

confidence: 99%

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer

Aladhadh

Alsanea²,

Aloraini

et al. 2022

Sensors

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Skin Cancer (SC) is considered the deadliest disease in the world, killing thousands of people every year. Early SC detection can increase the survival rate for patients up to 70%, hence it is highly recommended that regular head-to-toe skin examinations are conducted to determine whether there are any signs or symptoms of SC. The use of Machine Learning (ML)-based methods is having a significant impact on the classification and detection of SC diseases. However, there are certain challenges associated with the accurate classification of these diseases such as a lower detection accuracy, poor generalization of the models, and an insufficient amount of labeled data for training. To address these challenges, in this work we developed a two-tier framework for the accurate classification of SC. During the first stage of the framework, we applied different methods for data augmentation to increase the number of image samples for effective training. As part of the second tier of the framework, taking into consideration the promising performance of the Medical Vision Transformer (MVT) in the analysis of medical images, we developed an MVT-based classification model for SC. This MVT splits the input image into image patches and then feeds these patches to the transformer in a sequence structure, like word embedding. Finally, Multi-Layer Perceptron (MLP) is used to classify the input image into the corresponding class. Based on the experimental results achieved on the Human Against Machine (HAM10000) datasets, we concluded that the proposed MVT-based model achieves better results than current state-of-the-art techniques for SC classification.

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Section: The Proposed Methodsmentioning

confidence: 99%

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer

Aladhadh

Alsanea²,

Aloraini

et al. 2022

Sensors

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“…Su et al [1] concentrated on the deep transfer learning (TL) for the recognition and categorization of FM. Habib et al [2] focused on the deep learning (DL) based model for the identification of FM.…”

Section: Related Workmentioning

confidence: 99%

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Jena¹,

K²,

Thinakaran³

2022

STAIQC

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The importance of face mask (FM) is a major concern for the entire human society in the current circumstances. All peopleshould wear FM in order to lower the chance of infection due to several diseases. It is very much essential to track the peoplewho have not worn the FM in different crowded places, so that warning can be given to them to wear FM in order to lower thespread of infection of different diseases. So, the classification of human face images (HFIs) into human face with mask images(HFWMIs) and human face without mask images (HFWOMIs) types is an essential requirement in this situation. In this work, amachine intelligent (MI) based approach is proposed for the classification of HFIs into HFWMIs and HFWOMIs types. Theproposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN),Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is comparedwith other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB),Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Theproposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 200HFWMIs and 200 HFWOMIs are taken from the Kaggle source. The performance of all the methods is assessed using theperformance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA,F1,PR and RC ascompared to other ML based methods such as LRG, SVMN,RFS, NNT,DTR,ADB, NBY, KNNHand SGD.

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“…The MobileNet model is devised for enhancing the real word outcome of the DL model with restricted hardware units (15,16) . Here, the ReLU activation function is substituted by ReLU6, and the BN process is performed, supporting the automated alteration of data distribution.…”

Section: Dlmn Based Feature Extractionmentioning

confidence: 99%

Deep Learning based Mobilenet with Deep Belief Network for Lung Cancer Diagnosis in IOT and Cloud Enabled Environment

Jeya¹,

Valluru²,

Sherin³

2022

IJST

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Background: The purpose of the present investigation is to unravel the complexity involved in Lung Cancer diagnosis by inculcating a new automated deep learning based MobileNet (DLMN) with deep belief network (DBN), called DLMN-DBN model in Internet of Things and cloud enabled environment. Methods: This paper presents an optimal DLMN-DBN model for lung cancer diagnosis where the parameters of DLMN-DBN model are optimized, and feature extraction and classification takes place by DLMN and DBN model respectively. The experimentation part takes place on four dimensions: 1) IoT devices enabled data acquisition for lung cancer diagnosis and the data are transmitted to the cloud server for diagnostic process, 2) the Guassian Filtering (GF) based preprocessing technique for noise removal, 3) feature extraction using DLMN model and 4) Optimal classification using DBN model. For experimental validation, an extensive experimentation analysis is performed to highlight the superior diagnostic outcome of the DLMN-DBN model. Findings:The experimental values stated that the DLMN-DBN model has resulted in superior results when compared with existing models with higher accuracy, sensitivity, and specificity of 95.55%, 93.94%, and 96.49% respectively. Novelty and applications: The new state of the art DLMN-DBN model and its robustness help general practitioners efficiently and effectively diagnose lung cancer conditions at the initial stage thereby reducing further complications and morbidity.

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An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Cited by 33 publications

References 46 publications

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer

An Effective Skin Cancer Classification Mechanism via Medical Vision Transformer

A Machine Intelligence Based Approach for the Classification of Human Face with Mask and without Mask

Deep Learning based Mobilenet with Deep Belief Network for Lung Cancer Diagnosis in IOT and Cloud Enabled Environment

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