Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Wahlang, Imayanmosha; Maji, Arnab Kumar; Saha, Goutam Kumar; Chakrabarti, Prąsun; Jasiński, Michał; Leonowicz, Zbigniew; Jasińska, Elżbieta

doi:10.3390/s22051766

Cited by 44 publications

(11 citation statements)

References 30 publications

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“…CNN Model. Since AlexNet 31 was put forward in 2012, deep convolution neural networks have become one of the important components of artificial intelligence. With the establishment of large-scale data sets and the improvement of computer capabilities (such as GPU parallelization), 32 image recognition and classification tasks based on deep learning have achieved better results.…”

Section: Cnn Model Of Slagging Prediction In the Pccmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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The pre-combustion chamber (PCC) is commonly used to ensure stable combustion in boilers. However, when a coal-fired boiler uses a PCC combustor, the cross-sectional area and volumetric heat load in the PCC are high, which is prone to slagging, affecting the safe and stable operation of the boiler. Therefore, developing a fast and accurate prediction model is very important for judging the degree of slagging on the wall of the PCC. In recent years, artificial intelligence (AI) has been widely used in the field of thermal engineering, especially in the prediction of slagging. However, currently, using neural networks to predict the degree of boiler slagging only inputs simple parameters such as silicon ratio and acid–base ratio, without considering the actual complex flow and combustion characteristics in the furnace. In order to improve the accuracy of boiler slagging prediction, a deep parallel residual convolution neural network (DPRCNN) is proposed for automatically identifying three types of boiler wall slagging degrees. First, we simulate the boiler combustion process under various operating and structural parameters and output a dataset. Second, experimental validation is used to numerically simulate typical operating conditions, verifying the accuracy of the resulting dataset, and the generated dataset is sent to the DPRCNN model for identification. Finally, a convolutional neural network incorporating parallel thinking is proposed to predict boiler slagging. The experimental results show that the accuracy, accuracy, and area under curve (AUC) of DPRCNN reach 100%, 100%, and 100%, verifying the applicability of deep learning technology to boiler slagging prediction.

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Section: Cnn Model Of Slagging Prediction In the Pccmentioning

confidence: 99%

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Zhang

Zhao

et al. 2023

ACS Omega

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“…However, a huge number of training images and the carefully constructed deep networks required for this approach [ 22 ]. Similarly, a state-of-the-art framework was designed by [ 23 ] in order to classify brain MRI along with gender and age. They utilized deep neural network, convolutional network, LeNet, AlexNet, ResNet, and SVM to classify abnormal and normal MRIs accurately.…”

Section: Literature Reviewmentioning

confidence: 99%

An Enhanced Machine Learning Approach for Brain MRI Classification

2022

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Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

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“…Another study used pre-trained AlexNet to categorize and segment tumors depending on Gray-Level Co-Occurrence Matrix (GLCM) features [16]. SVM [17], CNN [18], other studies include Recurrent Neural Network (RNN) [19], AlexNet transfer learning network of CNN [20], VGG-16, Inception V3 and ResNet50 [21], along with CNN quartet technique.…”

Section: Literature Surveymentioning

confidence: 99%

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

Khuntia¹,

Sahu²,

Devi³

2022

IJACSA

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Brain tumors are the most widespread as well as disturbing sickness, among a very precise expectancy of life almost in their serious structure. As a consequence, therapy planning is a critical component in enhancing the characteristics of the patient's life. Image modalities like computed tomography (CT), magnetic resonance imaging (MRI), along with ultrasound images are commonly used to assess malignancies in the brain, breast, etc. MRI scans, in evidence, are employed in this study to identify the brain tumors. The application of excellent categorization systems on Magnetic Resonance Imaging (MRI) aids in the accurate detection of brain malignancies. The large quantity of data produced through MRI scan, on the other hand, renders physical distribution of tumor and non-tumor in a given time period impossible. It does, however, come with major obstruction. As a consequence, in order to decrease human mortality, a dependable and automated categorizing approach is necessary. The enormous geological and anatomical heterogeneity of the environment surrounding the brain tumor makes automated classification of brain tumor a difficult undertaking. This paper proposes a classification of Convolutional Neural Networks (CNN) for automated brain tumour diagnosis. To study as well as compare the findings, other convolutional neural network designs such as MobileNet V2, ResNet101, and DenseNet121 are used. Small kernels are employed to carry out the more intricate architectural design. This experiment was carried out using Python and Google Colab. The weight of a neuron is characterized as minute.

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Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Cited by 44 publications

References 30 publications

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

Study on Slagging Characteristics of Boiler Pre-combustion Chambers Based on Deep Learning

An Enhanced Machine Learning Approach for Brain MRI Classification

Novel Strategies Employing Deep Learning Techniques for Classifying Pathological Brain from MR Images

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