C-SAN: Convolutional stacked autoencoder network for brain tumor detection using MRI - Gayathiri, R.; Santhanam, Suganthi (Biomedical signal processing and control, 2025)
•Pre-processing is done by Non-Local Means (NLM) filter.•Image segmentation is done using V-Net.•Brain tumour is achieved by proposed C-SAN. Magnetic Resonance Imaging (MRI) is a medical imaging technique that uses strong magnetic fields and radio waves to generate detailed images of the brain and other organs. Brain tumor is the enlargement of abnormal cells that leads to cancer. The detail about abnormal tissue growth in brain is recognized using MRI. An MRI image is a detailed, three-dimensional image of the inside of the body produced by a non-invasive medical imaging procedure. The development of MRI technology is highly complex and is continually being refined by researchers to equip doctors with enhanced capabilities for patient treatment. Accordingly, this paper proposes a Convolutional stacked Autoencoder Network (C-SAN) for brain tumor detection employing MRI image. First, the acquired image is subjected to preprocessing, which is done by Non-Local Means (NLM) filter. After that, the filtered image is subjected to segmentation phase, which is done by V-Net. The segmented image is then allowed to feature extraction, where features of the image, such as grey level difference statistics, statistical features including coarseness, dissimilarity, autocorrelation and homogeneity, Mean-Variance and Median based Local binary pattern (MVM-LBP) with Discrete Wavelet Transform (DWT), Histogram of Gradients (HOG) and Local Vector Pattern (LVP) are extracted. Finally, brain tumor detection is performed by C-SAN, which is devised by integrating Convolutional Neural Network (CNN) and Deep Stacked Autoencoder (DSAE). The performance of the proposed method is analyzed using metrics, such as accuracy, sensitivity, and specificity. The proposed C-SAN obtained the values of 0.909, 0.958, and 0.928 for accuracy, sensitivity, and specificity, respectively.