Brain tumor detection using convolutional neural network
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%
"Brain tumor detection using convolutional neural network", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.8, Issue 3, page no.c223-c228, March-2023, Available :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: 000118830
Research Area: Computer Engineering
Country: VISAKHAPATNAM, ANDHRA PRADESH, India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
