Brain Tumors Diagnosis and Prediction Based on Applying the Learning Metaheuristic Optimization Techniques of Particle Swarm, Ant Colony and Bee Colony

Aly, Rabab Hamed M.; Rahouma, Kamel H.; Hamed, Hesham F. A.

doi:10.1016/j.procs.2019.12.098

Cited by 21 publications

(4 citation statements)

References 15 publications

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“…WOA approach, the proposed method performed better. The proposed method achieved 98.8% accuracy and the existing methods are achieved 97.5% by S. Shanthi et al (2022) [25], 98.0% by Kamel.H (2019) [20], 81.3% by D. Rammurthy (2022) [23].…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Magnetic resonance imaging segmentation for brain tumours not only detects the growth but also provides a better description of the centre and decorative growth. A brain convolution-based approach utilizes Glioma Mind Cancer Division Organizations in Attractive Reverberation Imaging, and the cycle is a mixture of different Convolution Brain Organization models that utilization nearby and worldwide information on cerebrum tissue to foresee the name of every pixel, consequently further developing outcomes [20]. The following are the contributions of this paper:…”

Section: Related Workmentioning

confidence: 99%

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Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

Agees Kumar C.

2024

jes

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Brain tumour segmentation is one of the most challenging problems in medical image analysis. To detect brain cancers, radiologists must use a computer-based tumour classification model. In medical imaging research, several computer-aided diagnostic (CAD) models are available to help radiologists with their patients. The reason for brain growth division is to deliver exact outline of brain tumour regions. This study suggests a Walrus Optimisation Algorithm approach for detecting brain tumours using MRI (Magnetic Resonance Imaging). Separating the characteristics into four categories has been utilized: no growth, gliomas, meningiomas, and pituitary cancers. The deep learning based model inception AlexNet and ResNet-18 is trained on an augmented training dataset. The CNN classifier is used for characteristics map improvement, while the LSTM (Long momentary memory) classifier is utilized for order. Besides, the boundary remembered for the classifiers is chosen aimlessly utilizing the Walrus Improvement Calculation to build the presentation of the CNN-LSTM classifier. The performance of the tumour diagnosis is assessed using the metrics: overall classification accuracy of 98.8%, precision of 96.23%, recall of 97.01%, specificity of 98%.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

Agees Kumar C.

2024

jes

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“…The recent research involves solutions improved through optimization algorithms. Techniques like Particle Swarm Optimization [18], Fire y optimization [16] and Ant-Bee colony optimization [17] when combined with neural networks give better classi cation accuracy rate.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

Dixit¹

2021

Preprint

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With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.

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“…in neural networks are Stochastic Gradient Descent(Sun et al, 2020), Ant Colony Optimization(Uthayakumar et al, 2020), and Particle Swarm Optimization(Aly et al, 2019). PSO outperforms other optimization techniques(Zhou et al, 2020).…”

mentioning

confidence: 99%

An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction

Maryam¹,

Anggoro

Tika

et al. 2022

Pak.j.stat.oper.res.

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Financial crisis prediction is a critical issue in the economic phenomenon. Correct predictions can provide the knowledge for stakeholders to make policies to preserve and increase economic stability. Several approaches for predicting the financial crisis have been developed. However, the classification model's performance and prediction accuracy, as well as legal data, are insufficient for usage in real applications. So that, an efficient prediction model is required for higher performance results. This paper adopts a novel two-hybrid intelligent prediction model using an Artificial Neural Network (ANN) for prediction and Particle Swarm Optimization (PSO) for optimization. At first, a PSO technique produces the hyperparameter value for ANN to fit the best architecture. They are weights and thresholds. Then, they are used to predict the performance of the given dataset. In the end, ANN-PSO generates predictions value of crisis conditions. The proposed ANN-PSO model is implemented on time series data of economic conditions in Indonesia. Dataset was obtained from International Monetary Fund and the Indonesian Economic and Financial Statistics. Independent variable data using 13 potential indicators, namely imports, exports, trade exchange rates, foreign exchange reserves, the composite stock price index, real exchange rates, real deposit rates, bank deposits, loan and deposit interest rates, the difference between the real BI rate and the real FED rate, the M1, M2 multiplier, and the ratio of M2 to foreign exchange reserves. Meanwhile, the dependent variable uses the perfect signal value based on the Financial Pressure Index. A detailed statistical analysis of the dataset is also given by threshold value to convey crisis conditions. Experimental analysis shows that the proposed model is reliable based on the different evaluation criteria. The case studies show that the result for predictive data is basically consistent with the actual situation, which has greatly helped the prediction of a financial crisis.

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Brain Tumors Diagnosis and Prediction Based on Applying the Learning Metaheuristic Optimization Techniques of Particle Swarm, Ant Colony and Bee Colony

Cited by 21 publications

References 15 publications

Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

Computer Aided Diagnosis of Brain Tumour Using Walrus Optimization Algorithm

Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

An Intelligent Hybrid Model Using Artificial Neural Networks and Particle Swarm Optimization Technique For Financial Crisis Prediction

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