Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach

Kakhandaki, Nita; Kulkarni, S.B.; Ramesh, K.; Kulkarni, Umakant P.

doi:10.4018/jcit.2019070104

Cited by 6 publications

(4 citation statements)

References 18 publications

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“…During this subsection, the proposed Weighted Naïve Bayes Module (WNBM) will be evaluated. WNBM is compared against the most recently used classification methods which are; (i) Enhanced K-Nearest Neighbors (EKNN) [52] , (ii) Naïve Bayes-Probabilistic Kernel Classifier (NB-PKC) [53] , and (iii) Whale Optimization Algorithm-SVM (WOA-SVM) [54] . Results are shown in table 15 and table 16 .…”

Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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COVID-19, as an infectious disease, has shocked the world and still threatens the lives of billions of people. Early detection of COVID-19 patients is an important issue for treating and controlling the disease from spreading. In this paper, a new strategy for detecting COVID-19 infected patients will be introduced, which is called Distance Biased Naïve Bayes (DBNB). The novelty of DBNB as a proposed classification strategy is concentrated in two contributions. The first is a new feature selection technique called Advanced Particle Swarm Optimization (APSO) which elects the most informative and significant features for diagnosing COVID-19 patients. APSO is a hybrid method based on both filter and wrapper methods to provide accurate and significant features for the next classification phase. The considered features are extracted from Laboratory findings for different cases of people, some of whom are COVID-19 infected while some are not. APSO consists of two sequential feature selection stages, namely; Initial Selection Stage (IS 2 ) and Final Selection Stage (FS 2 ). IS 2 uses filter technique to quickly select the most important features for diagnosing COVID-19 patients while removing the redundant and ineffective ones. This behavior minimizes the computational cost in FS 2 , which is the next stage of APSO. FS 2 uses Binary Particle Swarm Optimization (BPSO) as a wrapper method for accurate feature selection. The second contribution of this paper is a new classification model, which combines evidence from statistical and distance based classification models. The proposed classification technique avoids the problems of the traditional NB and consists of two modules; Weighted Naïve Bayes Module (WNBM) and Distance Reinforcement Module (DRM). The proposed DBNB tries to accurately detect infected patients with the minimum time penalty based on the most effective features selected by APSO. DBNB has been compared with recent COVID-19 diagnose strategies. Experimental results have shown that DBNB outperforms recent COVID-19 diagnose strategies as it introduce the maximum accuracy with the minimum time penalty.

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Section: Resultsmentioning

confidence: 99%

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Shaban¹,

Rabie

Saleh

et al. 2021

Pattern Recognition

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“…The experimental analysis shows that this system has an accuracy rate of 98%. Kakhandaki et al [28] perform brain Haemorrhages MRI classification using Naïve Bayes-Probabilistic Kernel Approach. Three stages followed to get desired results are pre-processing, segmentation and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

Muneer Butt,

Arif,

Letchmunan

et al. 2023

Computers, Materials &Amp; Continua

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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-theart, 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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“…Second, apply the CNN on these image segmentation also known as U-Net and performance is evaluated by 5 fold cross-validation with a dice coefficient of 0.31. A novel approach NB-PKC [55] used to apply an image mask on MRI images for the detection of brain hemorrhage. The binary thresholding process used to get minimal local binary pattern and GLCM for segmentation but 13% results improved than the usual Support Vector Machine (SVM) algorithm, still not able to detect very small subarachnoid hemorrhage.…”

Section: Related Workmentioning

confidence: 99%

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

et al. 2021

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Brain Hemorrhage is the eruption of the brain arteries due to high blood pressure or blood clotting that could be a cause of traumatic injury or death. It is the medical emergency in which a doctor also need years of experience to immediately diagnose the region of the internal bleeding before starting the treatment. In this study, the deep learning models Convolutional Neural Network (CNN), hybrid models CNN+LSTM and CNN+GRU are proposed for the Brain Hemorrhage classification. The 200 head CT scan images dataset is used to boost the accuracy rate and computational power of the deep learning models. The major aim of this study is to use the abstraction power of deep learning on a set of fewer images because in most crucial cases extensive datasets are not available on the spot. The image augmentation and imbalancing the dataset methods are adopted with CNN model to design a unique architecture and named as Brain Hemorrhage Classification based on Neural Network (BHCNet). The performance of the proposed approach are analyzed in terms of accuracy, precision, sensitivity, specificity and F1-score. Further, the experimental results are evaluated by comparative analyses of the balanced and imbalanced dataset with CNN, CNN+LSTM and CNN+GRU models. The promising results are achieved with CNN by imbalancing the dataset and gain highest accuracy that outperforms the hybrid CNN+LSTM and CNN+GRU models. The results reveals the effectiveness of the proposed model for accurate prediction to save the life of the patient in the meantime and fast employment in the real life scenario.

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Classification of Brain Hemorrhages in MRI Using Naïve Bayes- Probabilistic Kernel Approach

Cited by 6 publications

References 18 publications

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Accurate detection of COVID-19 patients based on distance biased Naïve Bayes (DBNB) classification strategy

Feature Enhanced Stacked Auto Encoder for Diseases Detection in Brain MRI

BHCNet: Neural Network-Based Brain Hemorrhage Classification Using Head CT Scan

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