Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

Gannour, Oussama El; Hamida, Soufiane; Cherradi, Bouchaib; Al-Sarem, Mohammed; Raihani, Abdelhadi; Saeed, Faisal; Hadwan, Mohammed

doi:10.3390/electronics11010103

Cited by 26 publications

(9 citation statements)

References 57 publications

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“…Perumal et al [14] suggested AlexNet paired with SVM model to identify COVID-19 using chest CT scan pictures with an accuracy of 96.69%. Further, a few research 46] employed multiple transfer learning models to identify COVID-19 patients.…”

Section: Literature Reviewmentioning

confidence: 99%

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

2022

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One of the most difficult research areas in today's healthcare industry to combat the coronavirus pandemic is accurate COVID-19 detection. Because of its low infection miss rate and high sensitivity, chest computed tomography (CT) imaging has been recommended as a viable technique for COVID-19 diagnosis in a number of recent clinical investigations. This article presents an Internet of Medical Things (IoMT)-based platform for improving and speeding up COVID-19 identification. Clinical devices are connected to network resources in the suggested IoMT platform using cloud computing. The method enables patients and healthcare experts to work together in real time to diagnose and treat COVID-19, potentially saving time and effort for both patients and physicians. In this paper, we introduce a technique for classifying chest CT scan images into COVID, pneumonia, and normal classes that use a Sugeno fuzzy integral ensemble across three transfer learning models, namely SqueezeNet, DenseNet-201, and MobileNetV2. The suggested fuzzy ensemble techniques outperform each individual transfer learning methodology as well as trainable ensemble strategies in terms of accuracy. The suggested MobileNetV2 fused with Sugeno fuzzy integral ensemble model has a 99.15% accuracy rate. In the present research, this framework was utilized to identify COVID-19, but it may also be implemented and used for medical imaging analyses of other disorders.

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Section: Literature Reviewmentioning

confidence: 99%

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

2022

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“…The graphical user interface also constructed by the researcher for the developed expert system. [37] designed a system to diagnose hepatitis B disease by using genetic neural network methodology. The clinical symptoms were used by the researchers as input in the developed diagnostic system.…”

Section: Adaptive Neuro-fuzzy Interence System (Anfis)mentioning

confidence: 99%

Intelligent Medical Diagnostic System for Hepatitis B

Singh¹,

Prashar²,

Singla³

et al. 2022

Computers, Materials &Amp; Continua

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The hepatitis B virus is the most deadly virus, which significantly affects the human liver. The termination of the hepatitis B virus is mandatory and can be done by taking precautions as well as a suitable cure in its introductory stage; otherwise, it will become a severe problem and make a human liver suffer from the most dangerous diseases, such as liver cancer. In this paper, two medical diagnostic systems are developed for the diagnosis of this life-threatening virus. The methodologies used to develop these models are fuzzy logic and the neuro-fuzzy technique. The diverse parameters that assist in the evaluation of performance are also determined by using the observed values from the proposed system for both developed models. The classification accuracy of a multilayered fuzzy inference system is 94%. The accuracy with which the developed medical diagnostic system by using Adaptive Network based Fuzzy Interference System (ANFIS) classifies the result corresponding to the given input is 95.55%. The comparison of both developed models on the basis of their performance parameters has been made. It is observed that the neuro-fuzzy technique-based diagnostic system has better accuracy in classifying the infected and non-infected patients as compared to the fuzzy diagnostic system. Furthermore, the performance evaluation concluded that the outcome given by the developed medical diagnostic system by using ANFIS is accurate and correct as compared to the developed fuzzy inference system and also can be used in hospitals for the diagnosis of Hepatitis B disease. In other words, the adaptive neuro-fuzzy inference system has more capability to classify the provided inputs adequately than the fuzzy inference system.

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“…Due to its higher performance in several fields as screening medical face mask [13], image description and a lot of challenges, the exploitation of DL technique in the medical image for classification, detection, and segmentation is highly encouraged [14]. In fact, various human diseases could be detected using such techniques, including COVID-19 [15]- [19], Parkinson's disease [20], breast cancer [21], diabetes diseases [22], medical image segmentation [23], and heart disease prediction [24]- [26]. A vast range of different scientific topics has developed because of advances in AI [27]- [35].…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Detection using MRI Images and Convolutional Neural Network

Lamrani¹,

Cherradi²,

Gannour³

et al. 2022

IJACSA

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A brain tumor is the cause of abnormal growth of cells in the brain. Magnetic resonance imaging (MRI) is the most practical method for detecting brain tumors. Through these MRIs, doctors analyze and identify abnormal tissue growth and can confirm whether the brain is affected by a tumor or not. Today, with the emergence of artificial intelligence techniques, the detection of brain tumors is done by applying the techniques and algorithms of machine learning and deep learning. The advantages of the application of these algorithms are the quick prediction of brain tumors, fewer errors, and greater precision, which help in decision-making and in choosing the most appropriate treatment for patients. In the proposed work, a convolution neural network (CNN) is applied with the aim of detecting the presence of a brain tumor and its performance is analyzed. The main purpose of this article is to adopt the approach of convolutional neural networks as a machine learning technique to perform brain tumor detection and classification. Based on training and testing results, the pre-trained architecture model reaches 96% in precision and classification accuracy rates. For the given dataset, CNN proves to be the better technique for predicting the presence of brain tumors.

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Concatenation of Pre-Trained Convolutional Neural Networks for Enhanced COVID-19 Screening Using Transfer Learning Technique

Cited by 26 publications

References 57 publications

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

Internet of Medical Things-Based COVID-19 Detection in CT Images Fused with Fuzzy Ensemble and Transfer Learning Models

Intelligent Medical Diagnostic System for Hepatitis B

Brain Tumor Detection using MRI Images and Convolutional Neural Network

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