Discrete Wavelet Transmission and Modified PSO with ACO Based Feed Forward Neural Network Model for Brain Tumour Detection

Jayalakshmi, M.; Rao, S. Nagaraja

doi:10.32604/cmc.2020.011710

Cited by 7 publications

(2 citation statements)

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“…The proposed model achieved 98.5 % accuracy at training phase and 84% accuracy at validation phase. The 2D-DWT [28] and Gabor filter are used for brain MR image classification in [15]. This research work achieved 92% accuracy by applying the back propagation neural networks to the system.…”

Section: Literature Surveymentioning

confidence: 97%

Brain Tumor Magnetic Resonance Image Classification: A Deep Learning Approach

Machiraju

Rao

2021

Preprint

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From the past decade, many researchers are focused on the brain tumor detection mechanism using magnetic resonance images. The traditional approaches follow the feature extraction process from bottom layer in the network. This scenario is not suitable to the medical images. To address this issue, the proposed model employed Inception-v3 convolution neural network model which is a deep learning mechanism. This model extracts the multi-level features and classifies them to find the early detection of brain tumor. The proposed model uses the deep learning approach and hyper parameters. These parameters are optimized using the Adam Optimizer and loss function. The loss function helps the machines to model the algorithm with input data. The softmax classifier is used in the proposed model to classify the images in to multiple classes. It is observed that the accuracy of the Inception-v3 algorithm is recorded as 99.34% in training data and 89% accuracy at validation data.

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Section: Literature Surveymentioning

confidence: 97%

Brain Tumor Magnetic Resonance Image Classification: A Deep Learning Approach

Machiraju

Rao

2021

Preprint

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“…However, deep neural networks need environment-specific design and training data with labels for supervised learning [16,17,20,27]. Evolutionary algorithms have advantages over global optimization [29,30] and can be used to generate environment-specific training data with labels for training supervised-learning-based deep neural networks in our future study. Motivated by these, we apply ABC to maximize the system sum data rates while ensuring the fairness threshold.…”

Section: Introductionmentioning

confidence: 99%

Novel Bee Colony Optimization with Update Quantities for OFDMA Resource Allocation

Sun

Huang

Wang

et al. 2021

Wireless Communications and Mobile Computing

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In recent years, large usage of wireless networks puts forward challenge to the utilization of spectrum resources, and it is significant to improve the spectrum utilization and the system sum data rates in the premise of fairness. However, the existing algorithms have drawbacks in efficiency to maximize the sum data rates of orthogonal frequency division multiple access (OFDMA) systems in the premise of fairness threshold. To address the issue, a novel artificial bee colony algorithm with update quantities of nectar sources is proposed for OFDMA resource allocation in this paper. Firstly, the population of nectar sources is divided into several groups, and a different update quantity of nectar sources is set for each group. Secondly, based on the update quantities of nectar sources set for these groups, nectar sources are initialized by a greedy subcarrier allocation method. Thirdly, neighborhood searches and updates are performed on dimensions of nectar sources corresponding to the preset update quantities. The proposed algorithm can not only make the initialized nectar sources maintain high levels of fairness through the greedy subcarrier allocation but also use the preset update quantities to reduce dimensions of the nectar sources to be optimized by the artificial bee colony algorithm, thereby making full use of both the local optimization of the greedy method and the global optimization of the artificial bee colony algorithm. The simulation results show that, just in the equal-power subcarrier allocation stage, the proposed algorithm can achieve the required fairness threshold and effectively improve the system sum data rates.

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