Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure it. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer's disease (AD), brain tumor, epilepsy, and Parkinson's disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment. INDEX TERMS Alzheimer's disease, brain tumor, deep learning, epilepsy, Parkinson's disease, machine learning NOMENCLATURE The most commonly used abbreviations in this survey are summarized in Table 1.

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“…In [118], [120], where the seed growing method was used for segmentation. The model was tested on 6 different BRATS datasets.…”

Section: ) Dl-based Approaches In Brain Tumor Diagnosismentioning

confidence: 99%

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

et al. 2021

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“…Enhancement is a more vital task for noise reduction that aids in the improvement of segmentation. Wavelet filter [50], median filter [7], Gaussian filter [52], PDDF filter, FNLM filter [49], and high-pass filter [7] are used in pre-processing step. Pereira et al [41] applied CNN with 3 kernel sizes and obtained 0.88, 0.83, 0.77 dice scores of complete, enhance, and non-enhanced tumor regions, respectively.…”

Section: Related Workmentioning

confidence: 99%

“…Deep CNN network has been applied for glioma classification. 3D-Unetwork has been used for glioma detection in which average global pooling layer is used for features mapping followed through 1 × 1 cascade convolutional work as FC layer [7]. A CNN model is utilized for deep features extraction and informative features selection using GA for glioma classification [12].…”

Section: Related Workmentioning

confidence: 99%

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Sharif

Amin

et al. 2021

Complex Intell. Syst.

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Brain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed to McCulloch's Kapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

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“…Then, they extracted some shape features from these images and classified them using the support vector machine and Feed Forward Network [22]. Javeria Amin et al proposed a different method for localization and classification of skin cancer [23]. They applied the PCA to the images and then obtained new features to detect the skin cancer [23].…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

“…Javeria Amin et al proposed a different method for localization and classification of skin cancer [23]. They applied the PCA to the images and then obtained new features to detect the skin cancer [23]. Ghasem Shakourian Ghalejoogh et al proposed the Stacking Ensemble Method based on the Meta Learning algorithm for skin disease classification from the dermoscopy images.…”

Section: Journal Of Artificial Intelligence and Systemsmentioning

confidence: 99%

Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All

Polat¹,

Koc²

2020

AIS

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The use of deep learning in the field of image processing is increasing. In this study, a new method based on Convolutional Neural Network is proposed to detect skin diseases automatically from Dermoscopy images. Skin cancer is one of the common diseases in the community. In this article, the skin images were taken from the data HAM10000 dataset prepared by Philipp Tschandl. There are seven classes in the skin disease data set; Actinic keratoses and intraepithelial carcinoma, Basal cell carcinoma, Benign keratosis, Dermatofibroma, Melanoma, Melanocytic type and Vascular lesions. To classify the skin diseases automatically, two different methods have been proposed: i) Alone Convolutional Neural Network model, and ii) the combination of Convolutional Neural Network and one-versus-all. In the proposed method, we have not used any pre-processing method to classify them. The raw dermatology images taken from the dataset have been given to the input of Convolutional Neural Network and then trained and tested by these images. In the second proposed method, seven different models having two-classes have been composed and then combined with the one-versus-all approach. While alone, Convolutional Neural Network obtained 77% classification accuracy in the detection of skin disease with seven classes, the combination of Convolutional Neural Network and one-versus-all approach achieved 92.90% accuracy. The obtained results have shown that the proposed method is very promising in the classification of skin disease from Dermoscopy images.

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Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

Cited by 114 publications

References 47 publications

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All

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