Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks

Anupama, C. S. S.; Sivaram, M.; Lydia, E. Laxmi; Gupta, Deepak; Shankar, K.

doi:10.1007/s00779-020-01492-2

Cited by 52 publications

(21 citation statements)

References 22 publications

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“…In Table 7, the accuracy of the proposed model has been compared with that obtained from other models in the field for Alzheimer's and Hemorrhage disease classification. The reported results are 93.18%, 98.01%, 96.36% and 96.50% of accuracy in case of Alzheimer's dataset [36][37][38][39] and 95.73%, 94.26%, and 95.5% of accuracy in case of Hemorrhage dataset [40][41][42]. In essence, the proposed model have achieved an average improvement accuracy of 4% than previously hybrid classification models.…”

Section: Performance Comparisonmentioning

confidence: 99%

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

et al. 2021

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Computer-aided diagnosis permits biopsy specimen analysis by creating quantitative images of brain diseases which enable the pathologists to examine the data properly. It has been observed from other image classification algorithms that the Extreme Learning Machine (ELM) demonstrates superior performance in terms of computational efforts. In this study, to classify the brain Magnetic Resonance Images as either normal or diseased, a hybridized Salp Swarm Algorithm-based ELM (ELM-SSA) is proposed. The SSA is employed to optimize the parameters associated with ELM model, whereas the Discrete Wavelet Transformation and Principal Component Analysis have been used for the feature extraction and reduction, respectively. The performance of the proposed “ELM-SSA” is evaluated through simulation study and compared with the standard classifiers such as Back-Propagation Neural Network, Functional Link Artificial Neural Network, and Radial Basis Function Network. All experimental validations have been carried out using two different brain disease datasets: Alzheimer’s and Hemorrhage. The simulation results demonstrate that the “ELM-SSA” is potentially superior to other hybrid methods in terms of ROC, AUC, and accuracy. To achieve better performance, reduce randomness, and overfitting, each algorithm has been run multiple times and a k-fold stratified cross-validation strategy has been used.

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Section: Performance Comparisonmentioning

confidence: 99%

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

et al. 2021

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“…The brain intracranial hemorrhage classification using synergic deep learning model is presented in [36]. Preprocessing is initially done using gabor filter and grab-cut based segmentation is used to identify the affected portion.…”

Section: Related Workmentioning

confidence: 99%

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Deepa¹,

Murugappan

Sumithra

et al. 2022

IEEE Access

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Magnetic Resonance Imaging (MRI) is a significant technique used to diagnose brain abnormalities at early stages. This paper proposes a novel method to classify brain abnormalities (tumor and stroke) in MRI images using a hybridized machine learning algorithm. The proposed methodology includes feature extraction (texture, intensity, and shape), feature selection, and classification. The texture features are extracted by intending a neoteric directional-based quantized extrema pattern. The intensity features are extracted by proposing the clustering-based wavelet transform. The shape-based extraction is preformed using conventional shape descriptors. Maximum A Priori (MAP) based firefly algorithm is proposed for feature selection. Finally, hybridized support vector-based random forest classifier is used for the classification. The MRI brain tumor and stroke images are detected and categorized into four classes which are a high-grade tumor, a low-grade tumor, an acute stroke, and a sub-acute stroke. Besides, three different regions are identified in tumor detection such as edema, and tumor (necrotic and non-enhancing) region. The accuracy of the proposed method is analyzed using various performance metrics in comparison with the few state-of-the-art classification methods. The proposed methodology successfully achieves a reliable accuracy of 88.3% for classifying brain tumor cases and 99.2% for brain stroke classification. The best F-score of 0.91 and the least FPR of 0.06 are attained while considering brain tumor classification against the proposed HSVFC. Likewise, HSVFC has 0.99 as the best F-score and a 0.0 FPR in the case of brain stroke classification. The experimental analysis offers a maximum mean accuracy of different classifiers for categorizing MRI brain tumor are 76.

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“…Irene et al [ 101 ] developed a dynamic graph convolutional neural network model (DGCNN) to segment the bleed regions, and achieved a sensitivity of 97.8%. Anupama et al [ 102 ] combined the GrabCut-based segmentation method and synergic deep learning to detect and classify five subtypes of hematoma. Watanabe et al [ 103 ] developed a CAD system using U-Net to detect hematoma and reduce the reading time consumed by the physicians.…”

Section: Generics Of Computer Aided Diagnosismentioning

confidence: 99%

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

Vidhya

Gudigar

Raghavendra

et al. 2021

IJERPH

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Traumatic brain injury (TBI) occurs due to the disruption in the normal functioning of the brain by sudden external forces. The primary and secondary injuries due to TBI include intracranial hematoma (ICH), raised intracranial pressure (ICP), and midline shift (MLS), which can result in significant lifetime disabilities and death. Hence, early diagnosis of TBI is crucial to improve patient outcome. Computed tomography (CT) is the preferred modality of choice to assess the severity of TBI. However, manual visualization and inspection of hematoma and its complications from CT scans is a highly operator-dependent and time-consuming task, which can lead to an inappropriate or delayed prognosis. The development of computer aided diagnosis (CAD) systems could be helpful for accurate, early management of TBI. In this paper, a systematic review of prevailing CAD systems for the detection of hematoma, raised ICP, and MLS in non-contrast axial CT brain images is presented. We also suggest future research to enhance the performance of CAD for early and accurate TBI diagnosis.

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Synergic deep learning model–based automated detection and classification of brain intracranial hemorrhage images in wearable networks

Cited by 52 publications

References 22 publications

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

On the Classification of MR Images Using “ELM-SSA” Coated Hybrid Model

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Automated Detection and Screening of Traumatic Brain Injury (TBI) Using Computed Tomography Images: A Comprehensive Review and Future Perspectives

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