Customized Deep Learning Approach for Brain Tumor Classification
Customized Deep Learning Approach for Brain Tumor Classification |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-11 |
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| Year of Publication : 2025 | ||
| Author : Vikram Verma and Alankrita Aggarwal | ||
| DOI : 10.14445/22315381/IJETT-V73I11P125 | ||
How to Cite?
Vikram Verma and Alankrita Aggarwal,"Customized Deep Learning Approach for Brain Tumor Classification", International Journal of Engineering Trends and Technology, vol. 73, no. 11, pp.349-363, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I11P125
Abstract
Magnetic Resonance Imaging (MRI) is widely accepted as the reference standard and a highly employed technique for brain tumor classification due to its ability to produce high-quality, non-invasive brain scans. Because tumor cells are heterogeneous, it is challenging to classify them; however, recent advancements in Machine Learning (ML) have enhanced the automation and accuracy of Brain Tumor Classification (BTC). Furthermore, with the expansion of artificial intelligence, particularly in Deep Learning (DL), a new avenue has opened, offering promising new opportunities for BT research and treatment. The objective of this research is to use multimodal images for the BTC. It specifically concentrates on MRI data collected from three different repositories. The novelty is in using these MRIs. Most of the earlier researches use single datasets or multiple datasets but applies DL individually. In this study, the MRI were mixed and then subjected to preprocessing before being used for training. The significant research gap is the absence of a unified framework for defining the most suitable neural network architecture for a given problem, which necessitates dependence on experimental trial-and-error strategies for new models. This study presents a Customized CNN (CCNN) solution for classifying 5712 brain MRI into four types. Besides CCNN, other Transfer Learning (TL) techniques like Custom VGG19 (C-VGG19), Customized MobileNet (C-MN), and customized DenseNet201 (C-DN201) are also used. According to trial data, test accuracy for the suggested CCNN was 95.80%, for C-VGG19 it was 97.02%, for C-MN it was 95.10%, and for C-DN201 it was 98.42%. DL frameworks utilizing CNN structures have been demonstrated to be highly effective for tumor classification and segmentation, successfully mitigating obstacles in MRI investigations.
Keywords
Brain Tumor, CNN, Deep Learning, Magnetic Resonance Imaging, Machine Learning.
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