Identification of Brain Tumor using Deep Learning Neural Networks

Alagarsamy, Saravanan; Kamatchi, Kartheeban; Selvaraj, Kailasam; Subramanian, Akshyaa; Fernando, Limsha Ravi; Kirthikaa, Rajamoorthy

doi:10.1109/incces47820.2019.9167685

Cited by 11 publications

(1 citation statement)

References 16 publications

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“…Saravanan Alagarsamy et al proposed the Identification of Brain Tumor using Deep Learning Neural Networks [1]. In this study, a CNN was used in this research, which is created using auto context and global and local picture attributes of 2 dimensional patches of various sizes.…”

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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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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Identification of high grade and low grade tumors in MR Brain Image using Modified Monkey Search Algorithm

Alagarsamy

Abitha

Ajitha

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Detection of tumors present in the Magnetic Resonance brain image is a challenging task in the research field of medical imaging processing. The tumors with distinguished boundaries are difficult to find in the MR brain images, while performing the manual segmentation process. There is a necessity of an automated segmentation technique for performing better segmentation in terms of tumors with distinguished boundaries. The automated modified monkey search technique is used to find the optimized cluster position, and a random search operation is performed is locate all the pixels present in the image and then finally the location of the tumor region is exactly segmented/predicted by using the suggested monkey search algorithm. The suggested technique will support the radiologist for finding the tumors with distinguishing boundaries and accuracy of prediction of tumors is also improved lot with this approach. Based on the early prediction of tumors, diagnosing procedures will save the lives of many human beings.

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Smart System Using Back Propagation Networks for Detection of Fire and Alert to the Users

Alagarsamy

Tilak

Kumar

et al. 2022

2022 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES)

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Identification of Brain Tumor using Deep Learning Neural Networks

Cited by 11 publications

References 16 publications

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

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

Identification of high grade and low grade tumors in MR Brain Image using Modified Monkey Search Algorithm

Smart System Using Back Propagation Networks for Detection of Fire and Alert to the Users

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