Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Mallick, Pradeep Kumar; Ryu, Seuc Ho; Satapathy, Sandeep Kumar; Mishra, Shruti; Nguyen, Gia Nhu; Tiwari, Prayag

doi:10.1109/access.2019.2902252

Cited by 230 publications

(100 citation statements)

References 33 publications

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“…De Albuquerque et al (2017) investigate the applications of brain computer interface systems. Some IoT frameworks are proposed to analyze the brain signals, such as brain CT images (Jaiswal et al, 2019;Sarmento et al, 2020;Vasconcelos et al, 2020), MRI (Mallick et al, 2019;Arunkumar et al, 2020), etc. Many applications benefit human daily life.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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“…where w is the n-dimensional vector and b is a scalar. Figure 3 represents the architecture of the DWA-DNN model for brain MRI image classification for disease detection based on DWA based DNN [11]. An image dataset is loaded for training the network.…”

Section: Dwt and Svm Based Classificationmentioning

confidence: 99%

“…Finally, only encoded approximation images are further used for training and testing of DNNs. For more details about this method, please refer to the [11].…”

Section: Dwt and Svm Based Classificationmentioning

confidence: 99%

“…Parameter setup for DWA DNN is used similar way as in [11]. In AE, total 5 layers were used with 64 × 64 encoded units.…”

Section: Dwa Dnn For Brain Mri Classificationmentioning

confidence: 99%

“…ANN is an interconnected group of these layers. A deep wavelet autoencoder (DWA) based DNN approach was introduced in [11]. An autoencoder (AE) is a type of ANN that learns efficient data in an unsupervised manner.…”

Section: Introductionmentioning

confidence: 99%

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Performance Analysis of Classification Techniques of Human Brain MRI Images

Chauhan

Choi

2019

IJFIS

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Artificial intelligence enhances the boundaries and capabilities of medical imaging. Hence, researchers are continuously attempting to develop an efficient and automated diagnosis system to increase the accuracy and performance to diagnose the brain abnormality. Therefore, it is required that a suitable method to diagnose and classify brain-related diseases such as Alzheimer disease, cancer, dementia, etc. Magnetic resonance imaging (MRI) is a powerful imaging technique in neuroscience for studying brain images. In the past years, many brain MRI classification techniques were proposed. Machine learning and deep learning have demonstrated a wonderful performance in the classification task. In this paper, the study of various brain MRI classification techniques has been provided. The aim of this study is to help the doctors/neurologists in selection of appropriate classification method based on several parameters like accuracy, computer complexity, and low training data availability. We also analyze and compare the performance of different classification methods based on several evaluation metrics.

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Detection of brain tumor size using modified deep learning and multilevel thresholding utilizing modified dragonfly optimization algorithm

Kumar

Kuttiappan

2022

Concurrency and Computation

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Nowadays, brain tumor (BT), which is the abnormal growth of the mass of tissues in the human brain, is the main cause of death among kids and adults. The existing methodologies concentrate only on the BT classification and could not detect the BT size. This work proposes to detect the BT size utilizing Modified Deep Learning (MDNN) and multilevel thresholding (MT) utilizing modified dragonfly optimization (MD‐MT) algorithm. Afterward, the Histogram Clipping (HC)‐based Contrast Limited Adaptive Histogram Equalization (CLAHE) approach is implemented for enhancing the contrast of the inputted image, and certain features are extracted as of those contrast‐enhanced images. Next, the image is classified as (i) tumor image and (ii) nontumor image with the aid of MDNN. The tumor part is segmented as the classified tumor affected image utilizing MD‐MT. Here, the proposed HC‐CLAHE attains a 99.74% Spearman Rank Correlation, which is greater among other methods while the proposed MD‐MT attains a higher accuracy (99.73%). For classification, the proposed MDNN and the existing Artificial Neural Network, K‐Nearest Neighbor, Support Vector Machine, and Naïve Bayes are contrasted grounded on their performance in respect of precision, f‐measure, and recall. The proposed work shows excellent performance during the experimental evaluation.

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Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network

Cited by 230 publications

References 33 publications

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Performance Analysis of Classification Techniques of Human Brain MRI Images

Detection of brain tumor size using modified deep learning and multilevel thresholding utilizing modified dragonfly optimization algorithm

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