Hybrid-Patch-Alex: A new patch division and deep feature extraction-based image classification model to detect COVID-19, heart failure, and other lung conditions using medical images

COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.

Securing health care data through blockchain enabled collaborative machine learning.

Transferring of data in machine learning from one party to another party is one of the issues that has been in existence since the development of technology. Health care data collection using machine learning techniques can lead to privacy issues which cause disturbances among the parties and reduces the possibility to work with either of the parties. Since centralized way of information transfer between two parties can be limited and risky as they are connected using machine learning, this factor motivated us to use the decentralized way where there is no connection but model transfer between both parties will be in process through a federated way. The purpose of this research is to investigate a model transfer between a user and the client(s) in an organization using federated learning techniques and reward the client(s) for their efforts with tokens accordingly using blockchain technology. In this research, the user shares a model to organizations that are willing to volunteer their service to provide help to the user. The model is trained and transferred among the user and the clients in the organizations in a privacy preserving way. In this research, we found that the process of model transfer between user and the volunteered organizations works completely fine with the help of federated learning techniques and the client(s) is/are rewarded with tokens for their efforts. We used the COVID-19 dataset to test the federation process, which yielded individual results of 88% for contributor a, 85% for contributor b, and 74% for contributor c. When using the FedAvg algorithm, we were able to achieve a total accuracy of 82%.

Large‐scaled detection of COVID‐19 from X‐ray using transfer learning

The pandemic cause as a result of the outbreak of COVID‐19 disease continues to burden the healthcare system despite several interventions using vaccines and other preventive measures. Healthcare settings adopted the use of reverse transcription‐polymerase chain reaction (RT‐PCR) which is hampered by so many challenges such as miss‐diagnosis, false positive results, high cost, especially for those in remote and rural areas, the need for trained medical pathologists, the use of chemicals, and a lack of point‐of‐care detection. The use of radiographic images as an alternative or confirmatory approach has offered medical experts another option, but has some limitations, such as misinterpretation, and can be tedious for analyzing thousands of cases. In order to bridge this gap, we applied two AlexNet models for the classification of different types of pneumonia, including COVID‐19 using X‐ray. Considering the fact that the majority of articles in the literature reported binary classifications of radiographic images. This article utilizes X‐ray images for classification of COVID‐19, non‐COVID‐19 viral pneumonia, bacterial pneumonia, and normal cases using the AlexNet‐SoftMax classifier and the AlexNet‐SVM classifier. The research also evaluates performance based on 5k‐fold and 10k fold cross validation (CV). The results achieved in terms of accuracy, sensitivity, and specificity based on 70:30 partition, 5k, and 10k CV have shown that the models outperformed the majority of the state‐of‐the‐art deep learning architectures. [ FROM AUTHOR] Copyright of International Journal of Imaging Systems & Technology is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script. During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute. Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment. The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased. In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters. The performance of the ensemble mechanism is significantly better than the single CNN model. This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches. The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers. © 2023 Lavoisier. All rights reserved.

DuaNet: A Novel Lightweight CNN Model for Classifying Five-class Lung Diseases

Lung diseases rank among the world's top killers and disablers. Therefore, early identification is crucial for improving long-term survival rates and boosting the chances of recovery. Unlike the traditional method, machine learning (ML) showed great success in the medical field, mainly detecting and diagnosing different diseases. Most recently, the deep learning approach enhanced classification accuracy and eliminated the difficulty of manual feature extraction. As a literature conclusion, the model performance accuracy is inversely proportional to the number of lung diseases under consideration. In addition, no more than four classes (including normal) were considered previously. This work developed a lightweight CNN model, identified as DuaNet, with higher accuracy than the up-to-the-date models. The dataset has 930 X-ray images, categorized into five-class lung diseases: normal, tuberculosis, pneumonia COVID-19, pneumonia viral, and pneumonia bacterial. DuaNet comprises fifteen layers involving input, seven convolutional blocks, three max-pooling, three fully connected, and one output (Softmax) layer. Each convolutional block consists of a convolutional layer, Batch normalization, and ReLU activation function. The final model (DuaNet) obtained a performance accuracy of 99.87%, with 100% for other metrics. © 2022 IEEE.

Novel Face Mask Detection Technique using Machine Learning to control COVID'19 pandemic.

The COVID-19 pandemic has been scattering speedily around the world since 2019. Due to this pandemic, human life is becoming increasingly involutes and complex. Many people have died because of this virus. The lack of antiviral drugs is one of the reasons for the spreading of COVID-19 virus. This disease is spreading continuously and easily due to some common mistakes by people, like breathing, coughing and sneezing by infected persons. The main symptom is the normal flu. Therefore, in the present condition, the best precaution for this disease is the face mask, which covers both areas of mouth & nose. According to the government and the World Health Organization, everyone should wear a face mask in busy places like hospitals and marketplaces. In today's environment, it's difficult to tell if someone is wearing a mask or not, and physical inspection is impractical since it adds to labour costs. In this research, we present a mask detector that uses a machine learning facial categorization system to determine whether a person is wearing a mask or not, so that it may be connected to a CCTV system to verify that only persons wearing masks are allowed in.

Diagnosis of Coronavirus Disease From Chest X-Ray Images Using DenseNet-169 Architecture.

The coronavirus disease (COVID-19) is a very contagious and dangerous disease that affects the human respiratory system. Early detection of this disease is very crucial to contain the further spread of the virus. In this paper, we proposed a methodology using DenseNet-169 architecture for diagnosing the disease from chest X-ray images of the patients. We used a pretrained neural network and then utilised the transfer learning method for training on our dataset. We also used Nearest-Neighbour interpolation technique for data preprocessing and Adam Optimizer at the end for optimization. Our methodology achieved 96.37 % accuracy which was better than that obtained using other deep learning models like AlexNet, ResNet-50, VGG-16, and VGG-19.

Learning effective embedding for automated COVID-19 prediction from chest X-ray images.

The pandemic that the SARS-CoV-2 originated in 2019 is continuing to cause serious havoc on the global population's health, economy, and livelihood. A critical way to suppress and restrain this pandemic is the early detection of COVID-19, which will help to control the virus. Chest X-rays are one of the more straightforward ways to detect the COVID-19 virus compared to the standard methods like CT scans and RT-PCR diagnosis, which are very complex, expensive, and take much time. Our research on various papers shows that the currently researchers are actively working for an efficient Deep Learning model to produce an unbiased detection of COVID-19 through chest X-ray images. In this work, we propose a novel convolution neural network model based on supervised classification that simultaneously computes identification and verification loss. We adopt a transfer learning approach using pretrained models trained on imagenet dataset such as Alex Net and VGG16 as back-bone models and use data augmentation techniques to solve class imbalance and boost the classifier's performance. Finally, our proposed classifier architecture model ensures unbiased and high accuracy results, outperforming existing deep learning models for COVID-19 detection from chest X-ray images producing State of the Art performance. It shows strong and robust performance and proves to be easily deployable and scalable, therefore increasing the efficiency of analyzing chest X-ray images with high accuracy in detection of Coronavirus.

Artificial intelligence based approach for categorization of COVID-19 ECG images in presence of other cardiovascular disorders.

Coronavirus disease (COVID-19) is a class of SARS-CoV-2 virus which is initially identified in the later half of the year 2019 and then evolved as a pandemic. If it is not identified in the early stage then the infection and mortality rates increase with time. A timely and reliable approach for COVID-19 identification has become important in order to prevent the disease from spreading rapidly. In recent times, many methods have been suggested for the detection of COVID-19 disease have various flaws, to increase diagnosis performance, fresh investigations are required. In this article, automatically diagnosing COVID-19 using ECG images and deep learning approaches like as Visual Geometry Group (VGG) and AlexNet architectures have been proposed. The proposed method is able to classify between COVID-19, myocardial infarction, normal sinus rhythm, and other abnormal heart beats using Lead-II ECG image only. The efficacy of the technique proposed is validated by using a publicly available ECG image database. We have achieved an accuracy of 77.42% using Alexnet model and 75% accuracy with the help of VGG19 model.

A robust semantic lung segmentation study for CNN-based COVID-19 diagnosis.

This paper aims to diagnose COVID-19 by using Chest X-Ray (CXR) scan images in a deep learning-based system. First of all, COVID-19 Chest X-Ray Dataset is used to segment the lung parts in CXR images semantically. DeepLabV3+ architecture is trained by using the masks of the lung parts in this dataset. The trained architecture is then fed with images in the COVID-19 Radiography Database. In order to improve the output images, some image preprocessing steps are applied. As a result, lung regions are successfully segmented from CXR images. The next step is feature extraction and classification. While features are extracted with modified AlexNet (mAlexNet), Support Vector Machine (SVM) is used for classification. As a result, 3-class data consisting of Normal, Viral Pneumonia and COVID-19 class are classified with 99.8% success. Classification results show that the proposed method is superior to previous state-of-the-art methods.

COVID Detection using Deep Learning

COVID-19 is a rapidly spreading pandemic, with the first cases being discovered in December 2019 Wuhan, China. CT scan images of the patient's lung are used where CNN algorithm is implemented. A comparative study of two more CNN models are used to evaluate this model (Resnet). The proposed model (Resnet) is capable of accurately predicting illness with an accuracy of 95.74%. This model can distinguish between covid, pneumonia, and normal CT scan pictures. Alexnet, Resnet, and Xception methods are utilised to compare the trained model to the input photos. Its then used to forecast the outcome. COVID/PNEUMONIA will be informed to the user through SMS based on CT scan findings. Result, availability of beds in the users' immediate vicinity, and hospital recommendations will be sent as an sms to the user. © 2022 IEEE.

COVID-19 classification using hybrid deep learning and standard feature extraction techniques

There is no doubt that COVID-19 disease rapidly spread all over the world, and effected the daily lives of all of the people. Nowadays, the reverse transcription polymerase chain reaction is the most way used to detect COVID-19 infection. Due to time consumed in this method and material limitation in the hospitals, there is a need for developing a robust decision support system depending on artificial intelligence (AI) techniques to recognize the infection at an early stage from a medical images. The main contribution in this research is to develop a robust hybrid feature extraction method for recognizing the COVID-19 infection. Firstly, we train the Alexnet on the images database and extract the first feature matrix. Then we used discrete wavelet transform (DWT) and principal component analysis (PCA) to extract the second feature matrix from the same images. After that, the desired feature matrices were merged. Finally, support vector machine (SVM) was used to classify the images. Training, validating, and testing of the proposed method were performed. Experimental results gave (97.6%, 98.5%) average accuracy rate on both chest X-ray and computed tomography (CT) images databases. The proposed hybrid method outperform a lot of standard methods and deep learning neural networks like Alexnet, Googlenet and other related methods. © 2023 Institute of Advanced Engineering and Science. All rights reserved.

Multi-class Classification of COVID-19 in Chest X-Ray Using Deep Convolutional Neural Networks

COVID-19 has principally affected everybody within the world in a way or another and thousands of individuals are becoming infected daily. In Present ways for checking COVID positive or negative, is taking a lot of time for results and these results are giving low specificity and sensitivity. Because of that the computer science—Artificial Intelligence (AI) is necessary in finding the positive COVID-19 cases. With Image processing and machine learning and deep learning techniques the researchers are able to achieve high accuracy and sensitivity and specificity from Chest X-ray (CXR) and Computed tomography (C.T) images. In this paper, we have proposed different deep neural networks like CNN, Alexnet, ResNet, Inception-v3 and ResNeXt-101-32x8d (all of those belong to the CNN family) with around 20,000+ CXR pictures of 3 classes. CXR is the initial technique which is important in diagnosing the Covid-19 patients. For verifying the strength of the models we compared validation accuracies, inception V3 achieved the best accuracy of 95%, however, we must always conjointly take into account the training time and complexity of the model. When the models accuracy, specificity, and sensitivity are higher, then it is really helpful for non-radiologist medical staff to diagnoses and quarantine faster when hospitals are flooded with patients, It reduces screening time for COVID-19 greatly. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Pneumonia and COVID-19 from Chest X-Ray Images Using Neural Networks and Deep Learning

Early detection of pneumonia and COVID-19 is extremely vital in order to guarantee timely access to medical treatment. Hence, it is necessary to detect pneumonia/COVID-19 from the X-ray images. In this paper, convolutional neural networks along with transfer learning are used to aid in the detection of the disease. A CNN model is proposed with four convolutional layers with four max pooling layers, one flatten layer followed by one fully connected hidden layer and output layer. Pre-trained models, namely AlexNet, InceptionV3, ResNet50, and VGG19 are implemented. Chest X-ray images (pneumonia), chest X-ray (COVID-19 and pneumonia), and COVID-19 radiography database are used for implementation for all the models. Precision, recall, and accuracy are used as performance evaluation metrices. The performance of all the models are compared. Experimental results show that the proposed CNN model outperforms all pre-trained models with improved accuracy with reduced trainable parameters. The highest accuracy achieved across all three datasets is 94.25% for the chest X-ray (COVID-19 and pneumonia) dataset. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Automatic Identification of Single Bacterial Colonies Using Deep and Transfer Learning

Bacterial classification is a vital step in medical diagnosis. This procedure normally has several stages. An early stage involves inspecting the morphology of the bacterial colonies. Traditionally, a bacterial colony expert inspects the sample to determine the type of bacteria through visual inspection or molecular biology techniques. With advances in image processing, specifically, the use of deep and transfer learning techniques, and the wide availability of cameras, we applied deep and transfer learning techniques to address this task without requiring expert knowledge or sample shipping. We used a convolutional neural network (CNN) to identify different bacterial colonies based on their appearance in images captured by cell phone cameras. In this paper, we collected a dataset that contains images of different bacteria taken by cell phone cameras with various settings. Thus, images of two classes of bacterial colonies were obtained in King Abdulaziz City for Science and Technology. The dataset contains 8,043 images. The experimental results show that our application has high accuracy without requiring expert inspections. Author

Mask Detection and Classification with Different Convolutional Neural Networks

Since 2019, the COVID-19 has been hanging over the whole world, causing uncountable financial loss. In this regard, wearing masks becomes a precaution for the public. However, some people are wearing masks in a wrong way, which may cause virus infection. To detect the wrong wearing of masks, we use 3 classic Convolutional Neural Networks, namely LeNet-5, AlexNet, and VGGNet-16, based on a unique dataset, to train the model and analyze the results. On the unique dataset, LeNet-5 achieved an accuracy of 80.3%, which was the lowest among the three networks, AlexNet attained an accuracy of 90.6%, which is near the precision of VGGNet-16, 92.83%. This work may help the advance of a digital city, making COVID-19 precaution under control. © 2022 SPIE.

A Methodology for Training Homomorphic Encryption Friendly Neural Networks

Privacy-preserving deep neural network (DNN) inference is a necessity in different regulated industries such as healthcare, finance, and retail. Recently, homomorphic encryption (HE) has been used as a method to enable analytics while addressing privacy concerns. HE enables secure predictions over encrypted data. However, there are several challenges related to the use of HE, including DNN size limitations and the lack of support for some operation types. Most notably, the commonly used ReLU activation is not supported under some HE schemes. We propose a structured methodology to replace ReLU with a quadratic polynomial activation. To address the accuracy degradation issue, we use a pre-trained model that trains another HE-friendly model, using techniques such as 'trainable activation' functions and knowledge distillation. We demonstrate our methodology on the AlexNet architecture, using the chest X-Ray and CT datasets for COVID-19 detection. Experiments using our approach reduced the gap between the F-1 score and accuracy of the models trained with ReLU and the HE-friendly model to within a mere 0.32-5.3% degradation. We also demonstrate our methodology using the SqueezeNet architecture, for which we observed 7% accuracy and F-1 improvements over training similar networks with other HE-friendly training methods.

COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images.

The novel coronavirus 2019 (COVID-19) spread rapidly around the world and its outbreak has become a pandemic. Due to an increase in afflicted cases, the quantity of COVID-19 tests kits available in hospitals has decreased. Therefore, an autonomous detection system is an essential tool for reducing infection risks and spreading of the virus. In the literature, various models based on machine learning (ML) and deep learning (DL) are introduced to detect many pneumonias using chest X-ray images. The cornerstone in this paper is the use of pretrained deep learning CNN architectures to construct an automated system for COVID-19 detection and diagnosis. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using the two modern pre-trained CNN models, AlexNet and Xception. Hence, we propose COVID-AleXception: a neural network that is a concatenation of the AlexNet and Xception models for the overall improvement of the prediction capability of this pandemic. To evaluate the proposed model and build a dataset of large-scale X-ray images, there was a careful selection of multiple X-ray images from several sources. The COVID-AleXception model can achieve a classification accuracy of 98.68%, which shows the superiority of the proposed model over AlexNet and Xception that achieved a classification accuracy of 94.86% and 95.63%, respectively. The performance results of this proposed model demonstrate its pertinence to help radiologists diagnose COVID-19 more quickly.

Classifiers Combination for Efficient Masked Face Recognition

This study was developed following the upheaval caused by the spread of the Coronavirus around the world. This global crisis greatly affects security systems based on facial recognition given the obligation to wear a mask. This latter, camouflages the entire lower part of the face, which is therefore a great source of information for the recognition operation. In this article, we have implemented three different pre-trained feature extractor models. These models have been improved by implementing the well-known Support Vector Machines (SVM) to reinforce the classification task. Among the investigated architectures, the FaceNet feature extraction model shows remarkable results on both databases with a recognition rate equal to 90% on RMFD and a little lower on SMFD with 88.57%. Following these simulations, we have proposed a combination of classifiers (SVM-KNN) that would prove a remarkable improvement and a significant increase in the accuracy rate of the selected model with almost 4% © 2022, International Journal of Advanced Computer Science and Applications.All Rights Reserved.

AI-driven deep and handcrafted features selection approach for Covid-19 and chest related diseases identification.

To identify various pneumonia types, a gap of 15% value is being created every five years. To fill this gap, accurate detection of chest disease is required in the healthcare department to avoid any serious issues in the future. Testing the affected lungs to detect a Coronavirus 2019 (COVID-19) using the same imaging modalities may detect some other chest diseases. This wrong diagnosis strongly needs a multidisciplinary approach to the right diagnosis of chest-related diseases. Only a few works till now are targeting pathological x-ray images. Many studies target only a single chest disease that is not enough to automate chest disease detection. Only a few studies regarding the observation of the COVID-19, but more cases are those where it can be misclassified as detecting techniques not providing any generic solution for all types of chest diseases. However, the existing studies can only detect if the person has COVID-19 or not. The proposed work significantly contributes to detecting COVID-19 and other chest diseases by providing useful analysis of chest-related diseases. One of our testing approaches achieves 90.22% accuracy for 15 types of chest disease with 100% correct classification of COVID-19. Though it analyzes the perfect detection as the accuracy level is high enough, but it would be an excellent decision to consider the proposed study until doctors can visually inspect the input images used by models that lead to its detection.