Open Access

ARTICLE

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

by Umair Muneer Butt^1,2,*, Rimsha Arif², Sukumar Letchmunan^1,*, Babur Hayat Malik², Muhammad Adil Butt²

1 School of Computer Sciences, Universiti Sains Malaysia, Penang, 1180, Malaysia
2 Department of Computer Science and IT, The University of Chenab, Gujrat, 50700, Pakistan

* Corresponding Authors: Umair Muneer Butt. Email: ; Sukumar Letchmunan. Email:

Computers, Materials & Continua 2023, 76(2), 2551-2570. https://doi.org/10.32604/cmc.2023.039164

Received 12 January 2023; Accepted 18 May 2023; Issue published 30 August 2023

Abstract

The detection of brain disease is an essential issue in medical and research areas. Deep learning techniques have shown promising results in detecting and diagnosing brain diseases using magnetic resonance imaging (MRI) images. These techniques involve training neural networks on large datasets of MRI images, allowing the networks to learn patterns and features indicative of different brain diseases. However, several challenges and limitations still need to be addressed further to improve the accuracy and effectiveness of these techniques. This paper implements a Feature Enhanced Stacked Auto Encoder (FESAE) model to detect brain diseases. The standard stack auto encoder’s results are trivial and not robust enough to boost the system’s accuracy. Therefore, the standard Stack Auto Encoder (SAE) is replaced with a Stacked Feature Enhanced Auto Encoder with a feature enhancement function to efficiently and effectively get non-trivial features with less activation energy from an image. The proposed model consists of four stages. First, pre-processing is performed to remove noise, and the greyscale image is converted to Red, Green, and Blue (RGB) to enhance feature details for discriminative feature extraction. Second, feature Extraction is performed to extract significant features for classification using Discrete Wavelet Transform (DWT) and Channelization. Third, classification is performed to classify MRI images into four major classes: Normal, Tumor, Brain Stroke, and Alzheimer’s. Finally, the FESAE model outperforms the state-of-the-art, machine learning, and deep learning methods such as Artificial Neural Network (ANN), SAE, Random Forest (RF), and Logistic Regression (LR) by achieving a high accuracy of 98.61% on a dataset of 2000 MRI images. The proposed model has significant potential for assisting radiologists in diagnosing brain diseases more accurately and improving patient outcomes.

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Keywords

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