Hybrid Deep Maxout-VGG-16 model for brain tumour detection and classification using MRI images

Brain tumor detection is essential to identify tumors at an early stage, allowing for more effective treatment. The patient's chances of recovery and survival can be improved by early detection. The existing methods for detecting brain tumour have several limitations, including limited accessib...

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Bibliographic Details
Published in:Journal of biotechnology Vol. 405; pp. 124 - 138
Main Authors: Loganayagi, T., Sravani, Meesala, Maram, Balajee, Rao, Telu Venkata Madhusudhana
Format: Journal Article
Language:English
Published: Netherlands Elsevier B.V 01.09.2025
Subjects:
Algorithms
biotechnology
brain
Brain - diagnostic imaging
brain neoplasms
Brain Neoplasms - diagnostic imaging
Brain tumour
Deep maxout network
Fuzzy Logic
geometry
Humans
hybrids
Image Interpretation, Computer-Assisted - methods
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
magnetism
Non-local mean filter
patients
Template–based K-means improved Fuzzy C means algorithm
Template–based K-means improved Fuzzy C means algorithm
Brain tumour
Magnetic resonance imaging
Deep maxout network
Non-local mean filter
ISSN:0168-1656, 1873-4863, 1873-4863
Online Access:Get full text
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Summary:Brain tumor detection is essential to identify tumors at an early stage, allowing for more effective treatment. The patient's chances of recovery and survival can be improved by early detection. The existing methods for detecting brain tumour have several limitations, including limited accessibility, exposure to radiation, high costs and potential for false negatives. To overcome the issues, a Deep Maxout-Visual Geometry Group-16 (DM-VGG-16) model is devised for detecting tumour in brain from Magnetic Resonance Imaging (MRI). Initially, MRI image is sent for pre-processing as input. Here, Non-Local Mean (NLM) filter performs pre-processing. The pre-processed image is subjected to segmentation stage, which is accomplished by Template–based K-means and improved Fuzzy C Means algorithm (TKFCM). Moreover, in feature extraction stage, various features, like area, cluster prominence, Hybrid PCA- Normalized GIST (NGIST) and Improved Median binary Pattern (IMBP) are extracted. Lastly, proposed DM-VGG-16 model is utilized for detection of brain tumors from extracted features. The DM-VGG-16 is the integration of Deep Maxout Network (DMN) and Visual Geometry Group-16 (VGG-16). The DM-VGG-16 outperformed superior results than conventional techniques with performance metrics, including accuracy, True Negative Rate (TNR) and True Positive Rate (TPR) of 90.76 %, 90.65 % and 90.75 % correspondingly. •DM-VGG-16 is used for the detection of brain tumor.•The segmentation of MRI images is performed using TKFCM.•Non-Local Mean (NLM) filter is used to pre-process MRI image.•DM-VGG-16 is designed by the integration of DMN and VGG-16.
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ISSN:0168-1656
1873-4863
1873-4863
DOI:10.1016/j.jbiotec.2025.05.009