Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning
The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CN...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Egyptian Informatics Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866524001403 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257675657412608 |
|---|---|
| author | M. Vimala SatheeshKumar Palanisamy Sghaier Guizani Habib Hamam |
| author_facet | M. Vimala SatheeshKumar Palanisamy Sghaier Guizani Habib Hamam |
| author_sort | M. Vimala |
| collection | DOAJ |
| description | The problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%. |
| format | Article |
| id | doaj-art-8e22ff5858614f40b14bdce8b26509ca |
| institution | OA Journals |
| issn | 1110-8665 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Egyptian Informatics Journal |
| spelling | doaj-art-8e22ff5858614f40b14bdce8b26509ca2025-08-20T01:56:20ZengElsevierEgyptian Informatics Journal1110-86652024-12-012810057710.1016/j.eij.2024.100577Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learningM. Vimala0SatheeshKumar Palanisamy1Sghaier Guizani2Habib Hamam3Department of ECE, P.S.R Engineering College, Sivakasi, Tamil Nadu 626140, IndiaDepartment of ECE, BMS Institute of Technology and Management, Bengaluru, India, 560119; Corresponding authors.College of Engineering, Alfaisal University, Riyadh 11533, Saudi Arabia; Corresponding authors.School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa; Faculty of Engineering, Uni de Moncton, NB 1EA 3E9, Canada; Hodmas University College, Taleh Area, Mogadishu, Somalia; Bridges for Academic Excellence, Tunis, TunisiaThe problem of brain tumor classification (BTC) has been approached with several methods and uses different features obtained from MRI brain scans. However, they suffer from achieving higher performance in BTC and produce poor performance with a higher false ratio. A convolutional neural network (CNN) based on BTC and a survival analysis model based on GDD (growth distribution depth) are presented. Initially, an adaptive median filter (AMF) is used to preprocess the MRI images in order to lower the amount of noise in the images. Secondly, in order to calculate the GDD value, the texture, shape, and gradient characteristics are extracted. Third, CNN is used to train the retrieved features based on the labels that were found. In the classification, the GDD features extracted are used to measure TSF (Tumor Support Factor) in each of them. The neurons of the network measure the value of tumor weight (TW) to perform classification. Additionally, the technique evaluates a patient’s survival and calculates the survival rate based on the TSF value of the growth characteristic. The multi-layer perceptron allows the computation of TW and supports the efficient performance of classification. The proposed method improves tumor classification performance by up to 97%.http://www.sciencedirect.com/science/article/pii/S1110866524001403Brain TumorImage ClassificationConvolution Neural NetworkGDD ApproximationSurvival Analysis |
| spellingShingle | M. Vimala SatheeshKumar Palanisamy Sghaier Guizani Habib Hamam Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning Egyptian Informatics Journal Brain Tumor Image Classification Convolution Neural Network GDD Approximation Survival Analysis |
| title | Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning |
| title_full | Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning |
| title_fullStr | Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning |
| title_full_unstemmed | Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning |
| title_short | Efficient GDD feature approximation based brain tumour classification and survival analysis model using deep learning |
| title_sort | efficient gdd feature approximation based brain tumour classification and survival analysis model using deep learning |
| topic | Brain Tumor Image Classification Convolution Neural Network GDD Approximation Survival Analysis |
| url | http://www.sciencedirect.com/science/article/pii/S1110866524001403 |
| work_keys_str_mv | AT mvimala efficientgddfeatureapproximationbasedbraintumourclassificationandsurvivalanalysismodelusingdeeplearning AT satheeshkumarpalanisamy efficientgddfeatureapproximationbasedbraintumourclassificationandsurvivalanalysismodelusingdeeplearning AT sghaierguizani efficientgddfeatureapproximationbasedbraintumourclassificationandsurvivalanalysismodelusingdeeplearning AT habibhamam efficientgddfeatureapproximationbasedbraintumourclassificationandsurvivalanalysismodelusingdeeplearning |