A customized VGG19 network with concatenation of deep and handcrafted features for brain tumor detection

Venkatesan Rajinikanth, Alex Noel Joseph Raj, Krishnan Palani Thanaraj, Ganesh R. Naik

Research output: Contribution to journalArticlepeer-review

93 Citations (Scopus)
234 Downloads (Pure)


Brain tumor (BT) is one of the brain abnormalities which arises due to various reasons. The unrecognized and untreated BT will increase the morbidity and mortality rates. The clinical level assessment of BT is normally performed using the bio-imaging technique, and MRI-assisted brain screening is one of the universal techniques. The proposed work aims to develop a deep learning architecture (DLA) to support the automated detection of BT using two-dimensional MRI slices. This work proposes the following DLAs to detect the BT: (i) implementing the pre-trained DLAs, such as AlexNet, VGG16, VGG19, ResNet50 and ResNet101 with the deep-features-based SoftMax classifier; (ii) pre-trained DLAs with deep-features-based classification using decision tree (DT), k nearest neighbor (KNN), SVM-linear and SVM-RBF; and (iii) a customized VGG19 network with serially-fused deep-features and handcrafted-features to improve the BT detection accuracy. The experimental investigation was separately executed using Flair, T2 and T1C modality MRI slices, and a ten-fold cross validation was implemented to substantiate the performance of proposed DLA. The results of this work confirm that the VGG19 with SVM-RBF helped to attain better classification accuracy with Flair (>99%), T2 (>98%), T1C (>97%) and clinical images (>98%).

Original languageEnglish
Article number3429
Number of pages13
JournalApplied Sciences
Issue number10
Publication statusPublished - 15 May 2020
Externally publishedYes

Bibliographical note

This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (http://creativecommons.org/licenses/by/4.0/)


  • Brain MRI slices
  • Brain tumor
  • Deep features
  • Features concatenation
  • Handcrafted-features
  • VGG19


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