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A Modified Deep Convolutional Neural Network for Abnormal Brain Image Classification
Open accessAbstract
Deep learning techniques have gained significant importance among artificial intelligence techniques for any computing applications. Among them, deep convolutional neural networks (DCNNs) is one of the widely used deep learning networks for any practical applications. The accuracy is generally high and the manual feature extraction process is not necessary in these networks. However, the high accuracy is achieved at the cost of huge computational complexity. The complexity in DCNN is mainly due to: 1) increased number of layers between input and output layers and 2) two set of parameters (one set of filter coefficients and another set of weights) in the fully connected network need to be adjusted. In this paper, the second aspect is targeted to reduce the computational complexity of conventional DCNN. Suitable modifications are performed in the training algorithm to reduce the number of parameter adjustments. The weight adjustment process in the fully connected layer is completely eliminated in the proposed modified approach. Instead, a simple assignment process is used to find the weights of this fully connected layer. Thus, the computational complexity is significantly reduced in the proposed approach. The application of modified DCNN is explored in the context of magnetic resonance brain tumor image classification. Abnormal brain tumor images from four different classes are used in this paper. The experimental results show promising results for the proposed approach.
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“Motivated by the dominant performance of CNNs, it is desirable to develop an end-to-end network for automatic placental maturity grading (PMG) from ultrasound images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [16]
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“They have been used successfully in a variety of medical imaging applications, including mammography [15], brain imaging [16], and prostate cancer detection [17].”
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Kang, J., Ullah, Z., & Gwak, J. (2021) · Sensors · doi:10.3390/s21062222 · Cited by 641
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Deep CNN for Brain Tumor Classification
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Improving Electric Energy Consumption Prediction Using CNN and Bi-LSTM
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Metadata
- Authors
- Hemanth, D. J., Anitha, J., Naaji, A., Geman, O., Popescu, D. E., & Hoang Son, L.
- Affiliations
- Karunya University (IN)
- Vasile Goldis Western University of Arad (RO)
- Ştefan cel Mare University of Suceava (RO)
- University of Oradea (RO)
- Division of Data Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 4275-4283
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2904910159
- Cited by
- 164
- References
- 20
- Citations by year
- 2026: 7 · 2025: 14 · 2024: 28 · 2023: 40 · 2022: 31 · 2021: 15 · 2020: 9 · 2019: 20
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- brain tumor classification, magnetic resonance imaging, computational complexity reduction, weight assignment, fully connected layers
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