BRAIN TUMOR DETECTION USING RECURRENT NEURAL NETWORK
Dr S. Hemalatha
, Dr A. Swaminathan2 , Jancy. A , Swetha. G. S
LSTM, RNN, TUMOR, MRI, CT, CNN, Brain tumor detection, Brain tumor segmentation, neoplasm, Deep learning.
Another common and deadly condition is brain tumours, which often only have a few years to live in their most severe stages. Planning therapy is so essential to improving living conditions for a patient. The techniques include ultrasonic imaging, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI) that can be used to evaluate tumours in several human organs, including the breast, prostate, liver, brain, and lungs. Many different types of brain malignancies are specifically diagnosed via MRI imaging. On the other hand, tumour or non-tumour segregation is permanently disrupted by the enormous amount of data that MRI scans acquire. Yet there are many issues with it because there are so few photographs. To prevent death, a trustworthy automatic separation mechanism is necessary. Automated brain tumour differentiation is extremely challenging since it is difficult to distinguish big regions from the surrounding many various types of brain tumour shapes. CNN is one particular
deep learning algorithm architecture that is used for operations like analyzing pixel data and picture recognition. In this study, we use RECURRENT NEURAL NETWORK that stimulates neuron activity in the human brain as it uses patterns to predict the next likely scenario. RNN saves the output of a particular layer and feeds this back to the input, so as to predict the output of the layer.
"BRAIN TUMOR DETECTION USING RECURRENT NEURAL NETWORK", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.703-710, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001111.pdf
Volume 11
Issue 2,
February-2024
Pages : 703-710
Paper Reg. ID: TIJER_151091
Published Paper Id: TIJERB001111
Downloads: 000267
Research Area: Science and Technology
Country: Thiruvallur,Chennai, Tamil Nadu, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
