Classification of Brain Tumor from Magnetic Resonance Imaging using Convolutional Neural Networks

Zyad, Mohammed-Amine; Gouskir, Mohamed; Bouikhalene, Belaid

doi:10.33832/ijast.2019.126.04

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Deep Neural Networks (DNN) [23], particularly Convolutional Neural Networks (CNN), have gained popularity for their efficacy in learning and recognizing features. Before the advent of deep learning, various approaches were employed for image classification into appropriate classes, including dance action identification [24], brain tumor identification [25][26][27], plant disease identification [28,29], among other applications [30,31]. Although CNNs prove computationally expensive and architecturally complex compared to other facial emotion recognition systems, these issues have been mitigated by recent technological advances and resource availability.…”

Section: Related Workmentioning

confidence: 99%

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis

Anji Reddy,

Regula,

Sharmila

et al. 2023

ISI

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The human face serves as a potent biological medium for expressing emotions, and the capability to interpret these expressions has been fundamental to human interaction since time immemorial. Consequently, the extraction of emotions from facial expressions in images, using machine learning, presents an intriguing yet challenging avenue. Over the past few years, advancements in artificial intelligence have significantly contributed to the field, replicating aspects of human intelligence. This paper proposes a Facial Emotion Recognition Convolutional Neural Network (FERCNN) model, addressing the limitations in accurately processing raw input images, as evidenced in the literature. A notable improvement in performance is observed when the input image is injected with noise prior to training and validation. Gaussian, Poisson, Speckle, and Salt & Pepper noise types are utilized in this noise injection process. The proposed model exhibits superior results compared to well-established CNN architectures, including VGG16, VGG19, Xception, and Resnet50. Not only does the proposed model demonstrate greater performance, but it also reduces training costs compared to models trained without noise injection at the input level. The FER2013 and JAFFE datasets, comprising seven different emotions (happy, angry, neutral, fear, disgust, sad, and surprise) and totaling 39,387 images, are used for training and testing. All experimental procedures are conducted via the Kaggle cloud infrastructure. When Gaussian, Poisson, and Speckle noise are introduced at the input level, the suggested CNN model yields evaluation accuracies of 92.17%, 95.07%, and 92.41%, respectively. In contrast, the highest accuracies achieved by existing models such as VGG16, VGG19, and Resnet 50 are 45.97%, 63.97%, and 54.52%, respectively.

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Section: Related Workmentioning

confidence: 99%

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis

Anji Reddy,

Regula,

Sharmila

et al. 2023

ISI

View full text Add to dashboard Cite

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“…where, q represents the number of neurons in the input layer; p represents the number of neurons in the output layer; c represents the constants between [1,10]. The decomposed data stream D is input to the neural network model, and the data stream is processed according to the neurons.…”

Section: ) Neural Network Link Load Predictionmentioning

confidence: 99%

“…Unbalanced data often exists in the medical field, that is, the types of data are not evenly distributed [1]- [3]. The amount of data in one category is abnormally larger than that in other categories [4], [5], occupying a dominant position.…”

Section: Introductionmentioning

confidence: 99%

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

2020

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In order to address the problem that the traditional algorithm can not predict the network link load effectively, which leads to high packet loss and energy loss, long turnaround time, slow stream rate and poor anti-attack ability, the paper proposes the stream algorithm of unbalanced medical big data based on convolutional neural network (CNN). The proposed algorithm included two stages:In the first stage, the decomposition-prediction model was constructed, the combined wavelet analysis and neural network analysis were used to complete the network link load prediction; In the second stage, based on the network link load situation, we analyzed the structure of each layer of convolution neural network, constructed the medical big data stream optimization model, introduced the ReLu function to calculate the convolution neural network, solved the optimization model, and completed the stream processing of unbalanced medical big data. The experimental results show that the network link load prediction accuracy of the proposed stream algorithm is as high as 93%, the lowest packet loss rate is only 2.0%, the energy loss of the stream process is low, the rate is fast, and the anti-attack efficiency is high, which is more conducive to the realization of data stream. INDEX TERMS Convolutional neural network, unbalanced medical big data, network load forecasting, ReLu function, data stream.

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MRI brain tumor medical images analysis using deep learning techniques: a systematic review

2021

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Classification of Brain Tumor from Magnetic Resonance Imaging using Convolutional Neural Networks

Cited by 4 publications

References 19 publications

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis

Performance Enhancement in Facial Emotion Classification Through Noise-Injected FERCNN Model: A Comparative Analysis

Stream of Unbalanced Medical Big Data Using Convolutional Neural Network

MRI brain tumor medical images analysis using deep learning techniques: a systematic review

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