Convolutional Neural Network based Image Classification and Detection of Abnormalities in MRI Brain Images

Krishnammal, P. Muthu; Raja, S. Selvakumar

doi:10.1109/iccsp.2019.8697915

Cited by 34 publications

(21 citation statements)

References 22 publications

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“…It shows human anatomy in all three planes; axial, sagittal, and coronal. It is used for quantitative analysis for most of the neurological diseases as brain [18]. Furthermore, it is able to detect streaming blood and secret vascular distortions.…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

“…It provides detail and enough information about different tissues inside human body with high contrast and spatial resolution subsequently. It engages broadly to anatomical auxiliary examination of the cerebrum tissues [18]. Bidani et al (2019) showed that MRI is important to diagnose dementia disease by scanning brain MRI which indicates by declining memory [22].…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

“…Muthu et al (2019) verified that appropriate hardware is beneficial of adequately localize brain tumor to achieve high accuracy of detection and classification using CNN [18].…”

Section: Medical Image Localizationmentioning

confidence: 99%

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Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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Section: Medical Image Modalitiesmentioning

confidence: 99%

Section: Medical Image Modalitiesmentioning

confidence: 99%

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Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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“…The results demonstrated deep features enabled classifiers to have comparable or better predictive power (95% accuracy) than conventional handcrafted feature extraction methods. P. Krishnammal and S. Raja [18] mainly put their focus in utilizing Convolutional Neural Network which makes use of the component maps preprocessed in Curvelet domain to classify the MRI brain image datasets. In this paper the feature extraction applied was found to be much better in terms of accuracy than traditional wavelet transforms because of its multi-directional feature.…”

Section: Figure 1 Digital Image Processing Systemmentioning

confidence: 99%

Comparative Study of Machine Learning Techniques in the Medical Field

Gaikwad¹,

Patel²,

Shetty³

2020

IJCSMC

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“…This system showed a classification accuracy of 72% for clinical data and 83% for simulated data. Krishnammal et al [9] suggested a system for identifying abnormalities in the brain using the Convolution Neural Network (CNN). The MRI images of the brain were taken from public databases and were preprocessed.…”

Section: Diagnosis Of Brain Diseases a Detection Of Brain Tumormentioning

confidence: 99%

Diagnosis of Brain Diseases using Neural Networks

2019

IJITEE

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Intensification in the occurrence of brain diseases and the need for the initial diagnosis for ailments like Tumor, Alzheimer’s, Epilepsy and Parkinson’s has riveted the attention of researchers. Machine learning practices, specifically deep learning, is considered as a beneficial diagnostic tool. Deep learning approaches to neuroimaging will assist computer-aided analysis of neurological diseases. Feature extraction of neuroimages carried out using Artificial Neural Networks leads to better diagnoses. In this study, all the brain diseases are revisited to consolidate the methodologies carried out by various authors in the literature.

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Convolutional Neural Network based Image Classification and Detection of Abnormalities in MRI Brain Images

Cited by 34 publications

References 22 publications

Review Research of Medical Image Analysis Using Deep Learning

Review Research of Medical Image Analysis Using Deep Learning

Comparative Study of Machine Learning Techniques in the Medical Field

Diagnosis of Brain Diseases using Neural Networks

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