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All Journal Indonesian Journal of Electrical Engineering and Informatics (IJEEI)
Ch Muralikrishna
SR University
Computer Science & IT Electrical & Electronics Engineering

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Brain Tumor Analysis Using Multi-Scale CNN and Gated DenseNet-169 Ch Muralikrishna; Shanker Chandre
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol 14, No 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7233

Abstract

Brain tumors refer to the uncontrolled growth of cells in the human brain, posing a significant risk to human health. Therefore, early detection of these tumors are crucial. However, existing feature extraction approaches often fail to capture the intricate characteristics of tumors, leading to suboptimal classification performance. To address this limitation, a Multi-scale Convolutional Neural Network and Gated Channel Transformation unit-based DenseNet-169 model (MC-GCT-DenseNet-169) is proposed for efficient brain tumor classification. The multi-scale convolutional neural network captures the nonlinear relationships between texture, shape, and intensity in magnetic resonance images (MRI). The GCT function in the DenseNet-169 model enhances the ability to learn subtle variations in MRI such as tumor texture and boundary features, which are essential for accurate classification. Experimental results show that the MC-GCT-DenseNet-169 method attains accuracy of 98.97% on the Figshare dataset, surpassing previous approaches such as Cross-prior Bayesian attention mechanism with a Correlated Inception and Residual Learning Network (CB-CIRL Net).
Brain Tumor Analysis Using Multi-Scale CNN and Gated DenseNet-169 Ch Muralikrishna; Shanker Chandre
Indonesian Journal of Electrical Engineering and Informatics (IJEEI) Vol. 14 No. 3: September 2026
Publisher : IAES Indonesian Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52549/ijeei.v14i3.7233

Abstract

Brain tumors refer to the uncontrolled growth of cells in the human brain, posing a significant risk to human health. Therefore, early detection of these tumors are crucial. However, existing feature extraction approaches often fail to capture the intricate characteristics of tumors, leading to suboptimal classification performance. To address this limitation, a Multi-scale Convolutional Neural Network and Gated Channel Transformation unit-based DenseNet-169 model (MC-GCT-DenseNet-169) is proposed for efficient brain tumor classification. The multi-scale convolutional neural network captures the nonlinear relationships between texture, shape, and intensity in magnetic resonance images (MRI). The GCT function in the DenseNet-169 model enhances the ability to learn subtle variations in MRI such as tumor texture and boundary features, which are essential for accurate classification. Experimental results show that the MC-GCT-DenseNet-169 method attains accuracy of 98.97% on the Figshare dataset, surpassing previous approaches such as Cross-prior Bayesian attention mechanism with a Correlated Inception and Residual Learning Network (CB-CIRL Net).
Co-Authors Shanker Chandre