A Robust Two-Stage Method for Accurate Brain Tumor Detection and Classification using Convolutional Neural Network and ResNet50

Registration ID: IJNRD_192713

Published ID: IJNRD2304596

DOI: Click Here to Get

Sunitha N V , Shravan J Poojary , Shrinidhi A , Vishwanath R

brain tumor, magnetic resonance imaging, resnet50, convolutional neural network

Brain tumors are a serious health concern that can affect individuals of all ages, and their diagnosis and treatment require prompt and accurate identification. Brain tumors are often diagnosed using magnetic resonance imaging (MRI), which is an extensively utilized diagnostic technique, but interpreting these images can be challenging and time-consuming for medical professionals. Recent advancements in machine learning techniques, particularly deep learning, have demonstrated potential for increasing the precision and effectiveness of brain tumor diagnosis. In this study, we present a two-stage approach using a convolutional neural network and transfer learning with fine-tuning in ResNet50 for identifying and categorizing brain tumors. The initial stage of the proposed approach involves the identification of brain tumors in the provided scan, while the second stage involves categorizing the tumors into one of three types: pituitary tumor, glioma, or meningioma. To train our detection model, we employed a dataset comprising 4,600 grayscale images of brain tumors. Meanwhile, for the classification model, the dataset is obtained from Figshare(a reputable data-sharing platform) which includes T1-weighted contrast-enhanced scans obtained from a total of 233 patients, with a total of 3,064 scans diagnosed with glioma, meningioma, and pituitary tumor. Specifically, the dataset contains 1426 scans from patients with glioma, 708 scans from patients with meningioma, and 930 scans from patients with a pituitary tumor. This study's goal is to evaluate the efficiency of machine learning methods in enhancing the detection and classification of brain tumors. We believe that our findings have significant implications for the development of tools and technologies that can aid medical professionals in accurately diagnosing, decision-making, and treating brain tumors, potentially leading to improved patient outcomes.

Published Paper   E-Certificate

Sunitha N V, Shravan J Poojary, Shrinidhi A, & Vishwanath R (April-2023). A Robust Two-Stage Method for Accurate Brain Tumor Detection and Classification using Convolutional Neural Network and ResNet50. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(4), f179-f186. https://ijnrd.org/papers/IJNRD2304596.pdf

Volume 8 Issue 4, April-2023

Pages : f179-f186

Paper Reg. ID: IJNRD_192713

Published Paper Id: IJNRD2304596

Downloads: 000121975

Research Area: Computer Science & Technology 

Country: Dakshina Kannada, Karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2304596.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2304596

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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