A deep autoencoder approach for detection of brain tumor images. (September 2022)

• Record Type: Journal Article
• Authors: Nayak, Dillip Ranjan; Padhy, Neelamadhab; Mallick, Pradeep Kumar; Singh, Ashish
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Brain tumor detection -- MRI images -- Spectral data augmentation -- Deep autoencoder -- Discrete wavelet transform; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108238
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Brain tumor detection received much attention due to its clinical significance for early treatment. Accurate diagnosis and classification of brain tumors are still challenging despite many major contributions are available. Existing methods mainly focus on accuracy in which classification problems like overfitting and underfitting have remained a major concern. This paper presents a technique for brain tumor identification using a deep autoencoder based on spectral data augmentation. In the first step, the morphological cropping process is applied to the original brain images to reduce noise and resize the images. Then Discrete Wavelet Transform (DWT) is used to solve the data-space problem with feature reduction. Finally, a dense layer is proposed for appropriate feature extraction and classification of the brain tumor images. The comparative analysis shows that the proposed algorithm outperforms with an accuracy of 97% and an AUC ROC score of 99.46%. © 2017 Elsevier Inc. All rights reserved. Graphical abstract: Image, graphical abstract