Design and Implementing Brain Tumor Detection Using Machine Learning Approach

Hemanth, G. R.; Janardhan, M.; Sujihelen, L.

doi:10.1109/icoei.2019.8862553

Cited by 119 publications

(29 citation statements)

References 8 publications

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“…The results are compared in Table 4 . After several data-collection and pre-processing steps such as average filtering segmentation, the DL model was implemented by researchers ( Hemanth et al, 2019 ). In comparison to existing approaches such as conditional random field (89%), SVM (84.5%), and genetic algorithm (GA) (83.64%), the research represents overall performance and comparative output on the brain MRI images.…”

Section: Discussionmentioning

confidence: 99%

Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data

Gaur

Bhandari²,

Razdan

et al. 2022

Front. Genet.

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Cancer research has seen explosive development exploring deep learning (DL) techniques for analysing magnetic resonance imaging (MRI) images for predicting brain tumours. We have observed a substantial gap in explanation, interpretability, and high accuracy for DL models. Consequently, we propose an explanation-driven DL model by utilising a convolutional neural network (CNN), local interpretable model-agnostic explanation (LIME), and Shapley additive explanation (SHAP) for the prediction of discrete subtypes of brain tumours (meningioma, glioma, and pituitary) using an MRI image dataset. Unlike previous models, our model used a dual-input CNN approach to prevail over the classification challenge with images of inferior quality in terms of noise and metal artifacts by adding Gaussian noise. Our CNN training results reveal 94.64% accuracy as compared to other state-of-the-art methods. We used SHAP to ensure consistency and local accuracy for interpretation as Shapley values examine all future predictions applying all possible combinations of inputs. In contrast, LIME constructs sparse linear models around each prediction to illustrate how the model operates in the immediate area. Our emphasis for this study is interpretability and high accuracy, which is critical for realising disparities in predictive performance, helpful in developing trust, and essential in integration into clinical practice. The proposed method has a vast clinical application that could potentially be used for mass screening in resource-constraint countries.

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Section: Discussionmentioning

confidence: 99%

Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data

Gaur

Bhandari²,

Razdan

et al. 2022

Front. Genet.

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“…The steps involved to preprocess Kaggle's brain image dataset [ 24 ] before applying CNN pretrained models are as follows: Import the packages needed. Import the two data folders with “Yes” and “No.” Read the images and transform them as labeled images (Tumor = Yes and No Tumor = No).…”

Section: Methodsmentioning

confidence: 99%

“…e steps involved to preprocess Kaggle's brain image dataset [24] before applying CNN pretrained models are as follows:…”

Section: Dataset Collection and Preprocessingmentioning

confidence: 99%

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images

Srinivas¹,

S.²,

Zakariah

et al. 2022

Journal of Healthcare Engineering

126

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Brain tumor classification is a very important and the most prominent step for assessing life-threatening abnormal tissues and providing an efficient treatment in patient recovery. To identify pathological conditions in the brain, there exist various medical imaging technologies. Magnetic Resonance Imaging (MRI) is extensively used in medical imaging due to its excellent image quality and independence from ionizing radiations. The significance of deep learning, a subset of artificial intelligence in the area of medical diagnosis applications, has macadamized the path in rapid developments for brain tumor detection from MRI to higher prediction rate. For brain tumor analysis and classification, the convolution neural network (CNN) is the most extensive and widely used deep learning algorithm. In this work, we present a comparative performance analysis of transfer learning-based CNN-pretrained VGG-16, ResNet-50, and Inception-v3 models for automatic prediction of tumor cells in the brain. Pretrained models are demonstrated on the MRI brain tumor images dataset consisting of 233 images. Our paper aims to locate brain tumors with the utilization of the VGG-16 pretrained CNN model. The performance of our model will be evaluated on accuracy. As an outcome, we can estimate that the pretrained model VGG-16 determines highly adequate results with an increase in the accuracy rate of training and validation.

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“…They analyzed and compared different methods like Relief F, Joint Mutual Information (JMI), Mutual Information Based Feature Selection (MIBFS), Minimum Redundancy Maximum Relevance (mRMR) and Conditional Mutual Information Maximization (CMIM). Similarly, Hemanth et al [22] designed and implemented the detection of the brain tumor by using the Convolution neural networks with other classifiers. They preprocessed the data with bilateral filter and then applied averaging filtering technique which was time consuming.…”

Section: Literature Reviewmentioning

confidence: 99%

Data Mining Approach for Detection and Classification of Brain Tumor

Faraz

Naz

Memon

2022

murjet

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Tumor is a mass or cells inside the brain that grows abnormally in one’s brain. Brain tumor is of two types primary and secondary. Primary tumors are hailed from brain cells and secondary tumors take place from cancer cells spread to one’s brain from other organs like lungs or breast. The Magnetic Resonance Imaging (MRI) is widely used because it gives high resolution and better-quality images. The main problem with the images is the inhomogeneity, unsharp boundaries and irregular noise which affects the results. Inhomogeneity means presence of some irrelevant information that must be removed. Unsharp boundaries are the most common problem in the images, they give blurry effect on the images that is why the information is not clear. To overcome these problems, we use the bilateral filter with the other techniques for the effective detection and segmentation. The proposed framework presents the detection and classification of the brain tumor. Bilateral filter is used to remove noise and preserves details. Bilateral filter is the best to preserve edges, sharpens the boundaries and takes care about the details of the image. By doing segmentation and classification we get the tumor detected.

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Design and Implementing Brain Tumor Detection Using Machine Learning Approach

Cited by 119 publications

References 8 publications

Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data

Explanation-Driven Deep Learning Model for Prediction of Brain Tumour Status Using MRI Image Data

Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images

Data Mining Approach for Detection and Classification of Brain Tumor

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