Brain Tumour Classification Using Deep Learning

Authors

  • Annapureddy Keerthi Reddy, P Naga Sudeeksha ,B Kanishka, R Haritha

Abstract

Tumour Means The Aggregation Of Abnormal Cells In Some Tissues Of The Organs. Tumour Can Be Cancerous Or Noncancerous. The Most Common Types Of Tumours Are Brain, Breast, Lung, Blood, Skin Tumour. Early Detection Of Tumour Cells Plays A Major Role In Treatment And Recovery Of Patient. Diagnosing A Tumour Usually Undergoes A Very Complicated And Time-Consuming Process. The Mri Images Of Various Patients At Various Stages Can Be Used For The Detection Of Tumours. There Are Various Types Of Feature Extraction And Classification Methods Which Are Used For Detection Of Tumour From Mri Images. Convolutional Neural Network Image Classification Algorithm Helps In Detecting The Tumour At Early Stage With High Accuracy. We Proposed A Recurrent Neural Network Architecture For Detection Of Tumour Cells Which Gives Accuracy Of About 90%. A Recurrent Neural Network (Rnn) Is A Type Of Artificial Neural Networks In That Connections Between Nodes Form A Directed Graph Along A Temporal Sequence.

Published

2021-07-20

How to Cite

Annapureddy Keerthi Reddy, P Naga Sudeeksha ,B Kanishka, R Haritha. (2021). Brain Tumour Classification Using Deep Learning. Drugs and Cell Therapies in Hematology, 10(1), 613–620. Retrieved from https://www.dcth.org/index.php/journal/article/view/144

Issue

Section

Articles