Brain Tumor detection from brain MRI using Deep Learning

Anil, Abhishek; Raj, Aditya; Sarma, Himangshu; R, Naveen Chandran; Deepa, P.

doi:10.29027/ijirase.v3.i2.2019.458-465

Cited by 19 publications

(9 citation statements)

References 2 publications

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“…Habib [1] utilised an artificial convolutional neural network (ANN) to identify tumours using a brain tumour dataset comparable to the one used in this article. During testing, he scored an accuracy of 88.7 percent.…”

Section: Methodsmentioning

confidence: 99%

Using Transfer Learning, a Mobile Application Detects Brain Tumors

R¹,

Pradepp²

2021

mjar

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The segmentation, identification, and extraction of contaminated tumour regions from magnetic resonance (MR) images is a serious problem, but it is a time-consuming and labor-intensive operation carried out by radiologists or clinical experts, whose accuracy is totally reliant on their knowledge. As a consequence, using computer-assisted technologies to circumvent these limits becomes more vital. In this study, we looked into Berkeley wavelet transformation (BWT) based brain tumour segmentation to improve performance and reduce the complexity of the medical image segmentation process. Furthermore, relevant properties are extracted from each segmented tissue to improve the support vector machine (SVM) based classifier's accuracy and quality rate. The experimental results of the recommended technique have been examined and validated for performance and quality analysis on magnetic resonance brain pictures based on accuracy, sensitivity, specificity, and dice similarity index coefficient. With 96.51 percent accuracy, 94.2 percent specificity, and 97.72 percent sensitivity, the recommended technique for discriminating normal and diseased tissues from brain MR images was shown to be effective. The results of the testing revealed an average dice similarity index coefficient of 0.82, showing that the automated (machine) extracted tumour area coincided with the manually determined tumour region by radiologists. The simulation results show the relevance of quality parameters and accuracy when compared to state-of-the-art approaches. The main objective is to develop a smartphone app for identifying brain tumours.

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Section: Methodsmentioning

confidence: 99%

Using Transfer Learning, a Mobile Application Detects Brain Tumors

R¹,

Pradepp²

2021

mjar

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“…The method proposed by Anil et al [15] consists of a classification network that divides the input MRI images into two groups: one that contains tumors and the other that does not. In this study, the classifier for brain cancer identification is retrained by applying the transfer learning approach.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

MRI-Based Brain Tumor Classification Using Dilated Parallel Deep Convolutional Neural Network with Ensemble of Machine Learning Classifiers

Rahman,

Islam,

Uddin

2024

Preprint

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Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) and better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. To handle gridding artifacts and extract both coarse and fine features from the images, this research suggests using a dilated parallel deep convolutional neural network (PDCNN) architecture that preserves a wide receptive field. To reduce complexity, initially, input images are resized and then grayscale transformed. Data augmentation has since been used to expand the number of datasets. Dilated PDCNN makes use of the lower computational overhead and contributes to the reduction of gridding artifacts. By contrasting various dilation rates, the global path uses a low dilation rate (2,1,1), while the local path uses a high dilation rate (4,2,1) for decremental even numbers to tackle gridding artifacts and extract both coarse and fine features from the two parallel paths. Using three different types of MRI datasets, the suggested dilated PDCNN with the average ensemble method performs better. The accuracy provided by the Multiclass Kaggle dataset-III, Figshare dataset-II, and Binary tumor identification dataset-I is 98.35%, 98.13%, and 98.67%, respectively. In comparison to state-of-the-art techniques, the suggested structure improves results by extracting both fine and coarse features, making it efficient.

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“…The YOLOv5 classification model was 85.95 percent accurate, while the FastAi classification model was 95.78 percent accurate. These two models can be used to identify brain tumours in real time and diagnose brain cancer early Abhishek Anil et al proposed the Brain Tumor detection from brain MRI using Deep Learning [5]. The proposed technique uses a classification network to divide the input MR images into two categories: one with tumor and one without.The model is again retrained using transfer learning [28] app from the classifier to identify the brain tumor.…”

Section: Related Workmentioning

confidence: 99%

Brain tumor segmentation and prediction on MRI images using deep learning network

Sangeetha¹,

Keerthika

Devendran³

et al. 2022

ijhs

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Brain Tumor is caused when the anomalousl cells that form within the brain and these could be of any size, shape in nature, so it is one of the difficult tasks to detect the presence of tumor. This could be found using MRI scans. In this paper, suitable algorithms have been developed to detect the MRI image has a brain tumor or not. The dataset used here has been taken from kaggle competition. Data augmentation is performed to maximize the data in dataset and this could results in having huge data. Since tumor area can overlap with non-tumor area of the MRI image, preprocessing steps is used to differentiate the images. So the proposed idea is to recognize tumors, this utilizes pre-processing strategies like filters, image enhancements, cropping, dilation, erosion, etc and for image classification pre-trained model InceptionResNetv2 is used as an CNN algorithm to detect whether the tumor is present or not. Various combination of pre-processing steps has been performed to find the effective pipeline for the classification. With the image pre processing techniques like cropped , median filter and CLAHE is gives a accuracy of 98.03% after the classification.

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Brain Tumor detection from brain MRI using Deep Learning

Cited by 19 publications

References 2 publications

Using Transfer Learning, a Mobile Application Detects Brain Tumors

Using Transfer Learning, a Mobile Application Detects Brain Tumors

MRI-Based Brain Tumor Classification Using Dilated Parallel Deep Convolutional Neural Network with Ensemble of Machine Learning Classifiers

Brain tumor segmentation and prediction on MRI images using deep learning network

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