Cited by 178Brain Tumor Detection and ClassificationMedical Image Segmentation TechniquesAI in cancer detection

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Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

Amin, Javaria, Sharif, Muhammad, Gul, Nadia, Raza, Mudassar, Anjum, Muhammad Almas, Nisar, Muhammad Wasif, Bukhari, Syed Ahmad Chan·2020·Journal of Medical Systems

DOI 10.1007/s10916-019-1483-2

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Abstract

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance.

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Authors: Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M. A., Nisar, M. W., & Bukhari, S. A. C.
Affiliations: COMSATS University Islamabad (PK)
Wah Medical College (PK)
National University of Sciences and Technology (PK)
St. John's University (US)
Corresponding author: Sharif, Muhammad
ORCID: Amin, Javaria
Sharif, Muhammad
Raza, Mudassar
Anjum, Muhammad Almas
Venue: Journal of Medical Systems
Publisher: Springer Science and Business Media LLC
Published: 2019-12-17
Volume: 44
Issue: 2
Pages: 32-32
ISSN: 0148-5598 (ISSN-L)
1573-689X
DOI: 10.1007/s10916-019-1483-2
OpenAlex: W2996632711
Cited by: 178
References: 61
Citations by year: 2026: 5 · 2025: 22 · 2024: 40 · 2023: 36 · 2022: 44 · 2021: 24 · 2020: 6 · 2019: 1
FWCI: 8.25
Type: journal article
Language: en
Open access: closed
Licence: www.springer.com/tdm (tdm)
www.springer.com/tdm (vor)
Locations: Journal of Medical Systems (publishedVersion)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords: brain tumor detection, stacked sparse autoencoder, MRI segmentation, image fusion, high-pass filter, median filter, BRATS dataset, tumor classification, medical image analysis
MeSH terms: Deep Learning, Algorithms, Brain Neoplasms, Diagnosis, Computer-Assisted, Humans, Image Processing, Computer-Assisted

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Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

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