Brain Tumor Classification Using Convolutional Neural Network
Abstract
Medical image classification has gained tremendous attention in recent years, and Convolutional Neural Network (CNN) is the most widespread neural network model for image classification problem. CNN is designed to determine features adaptively through backpropagation by applying numerous building blocks, such as convolution layers, pooling layers, and fully connected layers. In this paper, we mainly focused on developing a CNN model for classifying brain tumors in T1-weighted contrast-enhanced MRI images. The proposed system consists of two significant steps. First, preprocess the images using different image processing techniques and then classify the preprocessed image using CNN. The experiment is conducted on a dataset of 3064 images which contain three types of brain tumor (glioma, meningioma, pituitary). We achieved a high testing accuracy of 94.39%, average precision of 93.33% and an average recall of 93% using our CNN model. The proposed system exhibited satisfying accuracy on the dataset and outperformed many of the prominent existing methods.
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The Figshare dataset consists of 708 meningiomas, 1426 gliomas, and 930 pituitary images.
“The data consisted of three different kinds of brain tumors: 708 meningiomas, 1426 gliomas, and 930 pituitary images.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [56]
The Figshare dataset contains 3064 T1-weighted contrast-enhanced images collected from 233 patients.
“a publicly available dataset from Figshare containing 3064 T1-weighted contrast-enhanced images collected from 233 patients.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [56]
The proposed method by Das et al. used only one dataset to measure effectiveness.
“The main limitation is that only one dataset was used to measure the effectiveness of the proposed method.”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [56]
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Naeem, A., Anees, T., Naqvi, R. A., & Loh, W.-K. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020275 · Cited by 103 · see citing works
- Authors
- Das, S., Aranya, O. F. M. R. R., & Labiba, N. N.
- Affiliations
- Khulna University of Engineering and Technology (BD)
- Venue
- 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 1-5
- OpenAlex
- W2994753452
- Cited by
- 236
- References
- 9
- Citations by year
- 2026: 14 · 2025: 31 · 2024: 42 · 2023: 60 · 2022: 48 · 2021: 26 · 2020: 13 · 2019: 2
- FWCI
- 13.03
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Genetics › Glioma Diagnosis and Treatment
- Keywords
- brain tumor classification, glioma, meningioma, pituitary tumors, contrast-enhanced T1-weighted imaging, convolutional neural networks, medical image classification, image preprocessing
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