Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method

Nayak, Deepak Ranjan; Dash, Ratnakar; Majhi, Banshidhar

doi:10.1016/j.patrec.2020.04.018

Cited by 50 publications

(29 citation statements)

References 22 publications

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“…To handle this problem, data augmentation technique has been widely employed which helps in expanding the number of images using a set of transformations while preserving class labels. Augmentation also increases variability in the images and serves as a regularizer at the dataset level [32] . The techniques adopted in this study for augmenting the training images are illustrated in Fig.…”

Section: Methodsmentioning

confidence: 99%

“…In TL, the knowledge gained by a DL model trained from a large dataset is used to solve a related task with a comparatively smaller dataset. This helps in eliminating the need for a large dataset and longer learning time as required by DL methods that are trained from scratch [11] , [32] , [33] . In this study, eight pre-trained models such as AlexNet [34] , VGG-16 [35] , GoogleNet [36] , MobileNet-V2 [37] , SqueezeNet [38] , ResNet-34 [39] , ResNet-50 [39] , and Inception-V3 [40] have been used for classification of COVID-19 from normal cases.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak

Sinha

et al. 2021

Biomedical Signal Processing and Control

Self Cite

306

193

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Nayak

Sinha

et al. 2021

Biomedical Signal Processing and Control

Self Cite

306

193

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“…In this strategy, knowledge of a deep learning network trained on a big dataset is used to perform a task on a small dataset. This eliminates the need for a big dataset required for training and also long-term training of deep learning methods [ 55 , 56 ]. Transfer learning provides the models trained on one million images in the Imagenet dataset and their weights to be used on other datasets.…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Akyol

Şen

2021

Interdiscip Sci Comput Life Sci

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Coronavirus disease, which comes up in China at the end of 2019 and showed different symptoms in people infected, affected millions of people. Computer-aided expert systems are needed due to the inadequacy of the reverse transcription-polymerase chain reaction kit, which is widely used in the diagnosis of this disease. Undoubtedly, expert systems that provide effective solutions to many problems will be very useful in the detection of Covid-19 disease, especially when unskilled personnel and financial deficiencies in underdeveloped countries are taken into consideration. In the literature, there are numerous machine learning approaches built with different classifiers in the detection of this disease. This paper proposes an approach based on deep learning which detects Covid-19 and no-finding cases using chest X-ray images. Here, the classification performance of the Bi-LSTM network on the deep features was compared with the Deep Neural Network within the frame of the fivefold cross-validation technique. Accuracy, sensitivity, specificity and precision metrics were used to evaluate the classification performance of the trained models. Bi-LSTM network presented better performance compare to DNN with 97.6% value of high accuracy despite the few numbers of Covid-19 images in the dataset. In addition, it is understood that concatenated deep features more meaningful than deep features obtained with pre-trained networks by one by, as well. Consequently, it is thought that the proposed study based on the Bi-LSTM network and concatenated deep features will be noteworthy in the design of highly sensitive automated Covid-19 monitoring systems.

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“…In this model, the last five layers are removed and eight new layers are appended. Nayak et al [12] implemented an identification technique through CNN with five layers. is technique comprised four convolutional layers and one fully connected layer.…”

Section: Related Workmentioning

confidence: 99%

“…(3) Sort I P ; (4) / * Selection * / (5) set F P � I P ; / * F P denotes final population * / (6) while c e ! � 0orl G �� true do 7/ * c e and l G represent children elimination and last generation * / (8) Generate random c; / * c denotes children * / (9) set c e � 0; (10) for eachc do (11) Compute the fitness of c; (12) if f c ≤ f F P [1] then (13) remove c; (14) set c e + � 1; (15) else (16) set F P � c; (17) end (18) end (19) / * Mutation * / (20) for crossover do (21) select c 1 and c 2 randomly; / * c 1 , c 2 , and c 3 are Mathematical Problems in Engineering ranked [32]. Finally, the most nondominated solution is returned as initial parameters of CNN.…”

Section: Multiobjective Fitness Functionmentioning

confidence: 99%

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

Shukla

Sandhu

Ahirwar

et al. 2021

Mathematical Problems in Engineering

120

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COVID-19 is a new disease, caused by the novel coronavirus SARS-CoV-2, that was firstly delineated in humans in 2019.Coronaviruses cause a range of illness in patients varying from common cold to advanced respiratory syndromes such as Severe Acute Respiratory Syndrome (SARS-CoV) and Middle East Respiratory Syndrome (MERS-CoV). The SARS-CoV-2 outbreak has resulted in a global pandemic, and its transmission is increasing at a rapid rate. Diagnostic testing and approaches provide a valuable tool for doctors and support them with the screening process. Automatic COVID-19 identification in chest X-ray images can be useful to test for COVID-19 infection at a good speed. Therefore, in this paper, a framework is designed by using Convolutional Neural Networks (CNN) to diagnose COVID-19 patients using chest X-ray images. A pretrained GoogLeNet is utilized for implementing the transfer learning (i.e., by replacing some sets of final network CNN layers). 20-fold cross-validation is considered to overcome the overfitting quandary. Finally, the multiobjective genetic algorithm is considered to tune the hyperparameters of the proposed COVID-19 identification in chest X-ray images. Extensive experiments show that the proposed COVID-19 identification model obtains remarkably better results and may be utilized for real-time testing of patients.

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Automated diagnosis of multi-class brain abnormalities using MRI images: A deep convolutional neural network based method

Cited by 50 publications

References 22 publications

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: A comprehensive study

Automatic Detection of Covid-19 with Bidirectional LSTM Network Using Deep Features Extracted from Chest X-ray Images

Multiobjective Genetic Algorithm and Convolutional Neural Network Based COVID-19 Identification in Chest X-Ray Images

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