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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Brain Tumor Detection and Segmentation Using Mask R-CNN |
| Authors Name: | Haidarali Tajuddin Mulla , Atish Vijay Kamble , Shreyas Satish Mahamuni , Suraj Ravindra Khare , Mrs. S. J. Chougule |
| Unique Id: | IJSDR2303063 |
| Published In: | Volume 8 Issue 3, March-2023 |
| Abstract: | One of the dreadful diseases that the world encounters moment is brain tumors. When abnormal cells form in the brain, it is called a brain tumor. There are a lot of variations in the sizes and positions of tumors, and hence this makes it really hard for a complete understanding of tumours. Radiologists can fluently diagnose the disease with the help of medical image techniques, but making this process automatic is obviously useful. Magnetic Resonance Imaging (MRI) is the most effective system for detecting brain tutors where, MRI images are trained and tested in order to descry the tumor. The automated system would be suitable to find and pinpoint the exact position of the tumor in an MRI image. Our study describes a method for segmenting abnormal brain tissues and determining whether the case has a tumor. This approach detects a unique area of the brain and forecasts the liability of a tumor developing there. Mask regional-based convolution neural network (Mask R-CNN) is a pre-trained deep neural network model that is used to distinguish objects from an image such as buses, animals, persons, trees, and other objects. In comparison to numerous other analogous methods based on MLP, VGG-16 model, and U-net model, we discovered that Mask R-CNN method performs the best. The clarity of the MRI scans has a big impact on the delicacy. The proposed system was suitable to outperform similar systems on the same dataset, achieving a 74 percent crossroad over Union (IoU) score on the reference dataset, Brain MRI Images for Brain Tumor Detection. The demand for effective computer-aided brain tumour segmentation techniques has increased vastly in recent times. Still, accurate brain tumour segmentation is still a challenge because of its structural complexity such as variations in position, size, shape, overlapping tumor boundaries with normal brain tissues, etc. Existing automated approaches for brain tumour detection can be broadly categorized into handwrought features and deep learning (DL) based approaches. Qasem et al. [1] used a watershed segmentation algorithm along with the KNN for brain tumour classification and segmentation. This method performs well on the selected MRI images and is unable to accurately segment the tumour regions on challenging images containing tumors with multiple structural complexities |
| Keywords: | Abstract, Introduction, Litrature Survey, Methodology, Result, Hardware & Software used in proposed system, Conclusion, References. |
| Cite Article: | "Brain Tumor Detection and Segmentation Using Mask R-CNN", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 3, page no.407 - 411, March-2023, Available :http://www.ijsdr.org/papers/IJSDR2303063.pdf |
| Downloads: | 000337214 |
| Publication Details: | Published Paper ID: IJSDR2303063 Registration ID:204412 Published In: Volume 8 Issue 3, March-2023 DOI (Digital Object Identifier): Page No: 407 - 411 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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