Jinal Pravin Gala
Hemant Mahendra Gupta
- Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai India
- Research Scholar MCA, Thakur Institute of Management Studies, Career Development & Research (TIMSCDR) Mumbai India
Abstract
Tumors are masses created when brain cells multiply uncontrollably. A brain tumor is the medical term for this condition. Brain tumors are a serious and aggressive disease that can lead to a reduced life expectancy. Developing a treatment plan is essential to raising a patient’s standard of living. Tumors in different regions of the body are evaluated using a variety of imaging techniques, with MRI pictures being utilized mostly for brain tumors. These techniques include Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound. Scanners have a hard time processing the massive amounts of data produced by MRI procedures. To overcome this limitation, automated classification methods are necessary to improve diagnosis and prevent deaths. Because the surrounding tissue is so variable in both space and structure, automatically recognizing brain tumors is a particularly difficult undertaking. The traditional techniques for diagnosing DNNs entail segmenting the data and extracting texture and form features using Fuzzy C Means (FCM). The data is subsequently classified using Deep Neural Networks or Support Vector Machines (SVMs). Many applications, such as vector quantization, approximation, data clustering, pattern matching, optimization functions, and classification algorithms, make extensive use of neural network designs and implementations. These techniques are simple, but they calculate slowly and with impreciseness. The use of Convolutional Neural Networks (CNN) for automatic brain tumor detection is suggested in this paper. Small neurons and kernels with small weights are used in the construction of the CNN architecture. In comparison to other cutting-edge techniques, the testing results demonstrate that the suggested method attained a high accuracy of 97.5% with minimal complexity.
Keywords: Neural networks, MRI, Brain tumor, Convolutional neural network, Brain imaging
[This article belongs to International Journal of Cheminformatics(ijci)]
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| Volume | 01 |
| Issue | 01 |
| Received | March 11, 2024 |
| Accepted | April 2, 2024 |
| Published | May 28, 2024 |