Analysis of Brain Tumor using Pre-trained CNN Models and Machine Learning Techniques

Kaur, Divjot; Goel, Silky; Nijhawan, Rahul; Gupta, Sujata

doi:10.1109/sceecs54111.2022.9741022

Cited by 6 publications

(2 citation statements)

References 23 publications

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“…This research provides an effective solution to the essential problem of accurately segmenting brain tumours in MRI images. The complexity of a brain tumour requires precise and timely diagnosis for effective treatment (Kaur et al, 2022). Manually segmenting data is a lengthy process and can lead to discrepancies between different observers.…”

Section: Motivationmentioning

confidence: 99%

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Brain tumour segmentation and Analysis using BraTS Dataset with the help of Improvised 2D and 3D UNet model

Gore¹

2023

Preprint

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Brain tumours must be accurately identified and located on magnetic resonance imaging (MRI) scans for proper diagnoses and treatments. Accuracy when segmenting these areas ensures doctors have a clear understanding of how much of the tumour needs to be removed or treated. In our research, we propose using an Enhanced 3D U-net Model for accurately segmenting and analysing brain tumours. The model has been trained using a dataset of brain MRI scans that have been merged and optimized from 2D and 3D spatial information and labelled with tumour indications. The efficacy of the suggested system is determined by computing several metrics, including loss, accuracy, mean IOU, precision, sensitivity, specificity, and Dice coefficient. We looked into SegNet as another architecture to compare with UNet. Examining the results of our 3D UNet model against SegNet could provide us with a better understanding of how effective our solution is for categorizing and analyzing brain tumours. Our findings indicate that the 3D UNet model proposed has a higher accuracy in tumour segmentation than other approaches, with excellent precision. This model offers fast processing, making it appropriate for real-life medical applications. The updated 3D UNet architecture could result in more precise and successful segmentation of brain tumours, thus resulting in better diagnosis and treatment plans. To segregate brain tumours and evaluate 3D MRI data, this research provides a new Convolutional Neural Network model, which represents a significant advancement in medical image processing.

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Section: Motivationmentioning

confidence: 99%

“…In applications involving medical image segmentation, CNNs have displayed excellent performance. (Kaur et al, 2022). The proposed 3D UNet model for brain tumour segmentation aims to leverage the power of CNNs (Ahmad et al, 2021; F. Wang et al, 2020), and by integrating 3D spatial information and improving the UNet architecture.…”

Section: Motivationmentioning

confidence: 99%