An Efficient Optimization Technique to Detect Brain Tumor from MRI Images

Narayana, T. Lakshmi; Reddy, T. Sreenivasulu

doi:10.1109/icssit.2018.8748288

Cited by 23 publications

(9 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They achieved a testing accuracy of 94.39% using the CNN model. Additionally, the authors of the paper [31] used a support vector machine and a genetic algorithm to segment and categorize brain MRI images. The results of classifying the brain MRI images into normal and abnormal cases were about 91% accurate.…”

Section: Related Workmentioning

confidence: 99%

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

Ibrahim,

Ghnemat,

Abu Al-Haija

2023

View full text Add to dashboard Cite

Convolutional Neural Networks (CNNs) have exhibited remarkable potential in effectively tackling the intricate task of classifying MRI images, specifically in Alzheimer’s disease detection and brain tumor identification. While CNNs optimize their parameters automatically through training processes, finding the optimal values for these parameters can still be a challenging task due to the complexity of the search space and the potential for suboptimal results. Consequently, researchers often encounter difficulties determining the ideal parameter settings for CNNs. This challenge necessitates using trial-and-error methods or expert judgment, as the search for the best combination of parameters involves exploring a vast space of possibilities. Despite the automatic optimization during training, the process does not guarantee finding the globally-optimal parameter values. Hence, researchers often rely on iterative experimentation and expert knowledge to fine-tune these parameters and maximize CNN performance. This poses a significant obstacle in developing real-world applications that leverage CNNs for MRI image analysis. This paper presents a new hybrid model that combines the Particle Swarm Optimization (PSO) algorithm with CNNs to enhance detection and classification capabilities. Our method utilizes the PSO algorithm to determine the optimal configuration of CNN hyper-parameters. Subsequently, these optimized parameters are applied to the CNN architectures for classification. As a result, our hybrid model exhibits improved prediction accuracy for brain diseases while reducing the loss of function value. To evaluate the performance of our proposed model, we conducted experiments using three benchmark datasets. Two datasets were utilized for Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and an international dataset from Kaggle. The third dataset focused on brain tumors. The experimental assessment demonstrated the superiority of our proposed model, achieving unprecedented accuracy rates of 98.50%, 98.83%, and 97.12% for the datasets mentioned earlier, respectively.

show abstract

Section: Related Workmentioning

confidence: 99%

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

Ibrahim,

Ghnemat,

Abu Al-Haija

2023

View full text Add to dashboard Cite

show abstract

“…Narayana and Reddy presented median-filter-based genetic algorithm (GA) segmentation. 11 The elements of the gray-level co-occurrence matrix (GLCM) has been used as features with a support vector machine (SVM) classifier that was tested on Harvard medical image dataset and achieved an accuracy of 91.23%. Minz and Mahobiya adopted median filter noise removal, and threshold segmentation for enhancing the tumor detection efficiency on the available public brain tumor MRI dataset.…”

Section: Related Workmentioning

confidence: 99%

“…Generally, a CNN model contains several layers, namely input, convolutional, pooling, fully-connected, and output layers . [11][12][13][14] The two proposed BRAIN-TUMOR-net and transfer-learning-based CNN models have the following main common architectures 21 :…”

Section: Classification Of Mr Imagesmentioning

confidence: 99%

Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques

Shoaib

Elshamy

Taha

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

Brain tumor is an acute cancerous disease that results from abnormal and uncontrollable cell division. Brain tumors are classified via biopsy, which is not normally done before the brain ultimate surgery. Recent advances and improvements in deep learning (DL) models helped the health industry in getting accurate diseases diagnosis. This article concentrates on the classification of magnetic resonance (MR) images.The objective is to differentiate between glioma tumors, meningioma tumors, pituitary tumors, and normal cases. Four deep convolutional neural networks are considered and compared in this article. These networks are inceptionresnetv2, inceptionv3, transfer learning, and BRAIN-TUMOR-net. A transfer-learning strategy is considered to enhance the performance of pre-trained models and save the time of training. The used dataset is the brain tumor magnetic resonance imaging dataset. It contains four classes including 826 MR images for glioma tumor, 822 MR images for meningioma tumor, 827 MR images for pituitary tumor, and 835 MR images for normal cases. Due to the limited number of images, we use the augmentation strategy to enlarge the size of the dataset. 75% of the data are considered for training and the other 25% are considered for testing. Segmentation of the classified results is performed. Simulation resultsprove that the DL model from scratch obtains the highest performance with the augmented data. In addition, a new practical implementation is presented for the proposed models.

show abstract

“…This system achieves a sensitivity of 89% and an accuracy of 85%. Narayana and Reddy ( 19 ) introduced a median filter GA segmentation technique for the segmentation operation. With an SVM classifier, the GLCM is used including the features.…”

Section: Related Workmentioning

confidence: 99%

Efficient framework for brain tumor detection using different deep learning techniques

Taher

Shoaib

Emara

et al. 2022

Front. Public Health

View full text Add to dashboard Cite

The brain tumor is an urgent malignancy caused by unregulated cell division. Tumors are classified using a biopsy, which is normally performed after the final brain surgery. Deep learning technology advancements have assisted the health professionals in medical imaging for the medical diagnosis of several symptoms. In this paper, transfer-learning-based models in addition to a Convolutional Neural Network (CNN) called BRAIN-TUMOR-net trained from scratch are introduced to classify brain magnetic resonance images into tumor or normal cases. A comparison between the pre-trained InceptionResNetv2, Inceptionv3, and ResNet50 models and the proposed BRAIN-TUMOR-net is introduced. The performance of the proposed model is tested on three publicly available Magnetic Resonance Imaging (MRI) datasets. The simulation results show that the BRAIN-TUMOR-net achieves the highest accuracy compared to other models. It achieves 100%, 97%, and 84.78% accuracy levels for three different MRI datasets. In addition, the k-fold cross-validation technique is used to allow robust classification. Moreover, three different unsupervised clustering techniques are utilized for segmentation.

show abstract

An Efficient Optimization Technique to Detect Brain Tumor from MRI Images

Cited by 23 publications

References 6 publications

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

Improving Alzheimer’s Disease and Brain Tumor Detection Using Deep Learning with Particle Swarm Optimization

Efficient deep learning models for brain tumor detection with segmentation and data augmentation techniques

Efficient framework for brain tumor detection using different deep learning techniques

Contact Info

Product

Resources

About