Brain tumor detection using convolutional neural network

Registration ID: IJNRD_189037

Published ID: IJNRD2303232

DOI: Click Here to Get

PALASA BHOOMIKA , NITYA SREE DAMUROTHU , PACHAVA LAKSHMI PRASANNA , PACHIGOLLA SREENIJA , DR.K.SOUMYA

Tensor flow, Cnn ResNet-v2, Brain tumor, Filtering, Smoothening filter, Morphology, Segmentation, ,Medical image, SVM

Brain Tumor segmentation is one of the most crucial and arduous tasks in the field of medical image processing as a human-assisted manual classification can result in inaccurate prediction and diagnosis. Moreover, it becomes a tedious task when there is a large amount of data present to be processed manually. Brain tumors have diversified appearance and there is a similarity between tumor and normal tissues and thus the extraction of tumor regions from images becomes complicated. In this thesis work, we developed a model to extract brain tumor from 2D Magnetic Resonance brain Images (MRI) by Fuzzy C-Means clustering algorithm which was followed by both traditional classifiers and deep learning methods. The experimental study was carried out on a real time dataset with diverse tumor sizes, locations, shapes, and different image intensities. In the traditional classifier part, we applied six traditional classifiers namely- Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Multi-layer Perceptron (MLP), Logistic Regression, Naive Bayes and Random Forest. Among these classifiers, SVM provided the best result. Afterwards, we moved on to Convolutional Neural Network (CNN) which shows an improvement in performance over the traditional classifiers. We compared the result of the traditional classifiers with the result of CNN. Furthermore, the performance evaluation was done by changing the split ratio of CNN and traditional classifiers multiple times. We also compared our results with the existing research works in terms of segmentation and detection and achieved better results than many state-of-the-art methods. For the traditional classifier part, we achieved an accuracy of 92.42% which was obtained by Support Vector Machine (SVM) and CNN gave an accuracy of 97.87%

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PALASA BHOOMIKA, NITYA SREE DAMUROTHU, PACHAVA LAKSHMI PRASANNA, PACHIGOLLA SREENIJA, & DR.K.SOUMYA (March-2023). Brain tumor detection using convolutional neural network. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), c223-c228. https://ijnrd.org/papers/IJNRD2303232.pdf

Volume 8 Issue 3, March-2023

Pages : c223-c228

Paper Reg. ID: IJNRD_189037

Published Paper Id: IJNRD2303232

Downloads: 000121989

Research Area: Computer Engineering 

Country: VISAKHAPATNAM, ANDHRA PRADESH, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303232.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303232

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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