Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification

Santhoshkumar, S.; Varadarajan, Vijayakumar; Gavaskar, S.; Amalraj, J. Jegathesh; Sumathi, A.

doi:10.3390/electronics10212574

Cited by 17 publications

(3 citation statements)

References 26 publications

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“…More specifically, in the context of machine learning, a learning algorithm utilizes a given dataset consisting of input data and their corresponding known responses (classes) to train a model as described in Figure 5. The objective is to enable the model to make dependable predictions about the behavior or outcome of new, unseen data [18], [19].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

Arabiat,

Altayeb

2024

IJECE

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The brain is one of the most important vital organs in the human body. It is responsible for most of the body’s basic activities, such as breathing, heartbeat, thinking, remembering, speaking, and others. It also controls the central nervous system. Cerebral hemorrhage is considered one of the most dangerous diseases that a person may be exposed to during his life. Therefore, the correct and rapid diagnosis of the hemorrhage type is an important medical issue. The innovation in this work lies in extracting a huge number of effective features from computed tomography (CT) images of the brain using the Orange3 data mining technique, as the number of features extracted from each CT image reached (1,000). The proposed system then uses the extracted features in the classification process through logistic regression (LR), support vector machine (SVM), k-nearest neighbor algorithm (KNN), and convolutional neural networks (CNN), which classify cerebral hemorrhage into four main types: epidural hemorrhage, subdural hemorrhage, intraventricular hemorrhage, and intraparenchymal hemorrhage. A total of (1,156) CT images were tested to verify the validity of the proposed model, and the results showed that the accuracy reached the required success level with an average of (97.1%).

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

Arabiat,

Altayeb

2024

IJECE

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“…A machine learning algorithm is presented in [ 18 ] to identify and classify ICHs. Tsallis entropy (TE) is used in conjunction with the grasshopper optimization algorithm (GOA) for the segmentation of CT images.…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Umapathy,

Murugappan,

Bharathi

et al. 2023

Diagnostics

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Diagnosing Intracranial Hemorrhage (ICH) at an early stage is difficult since it affects the blood vessels in the brain, often resulting in death. We propose an ensemble of Convolutional Neural Networks (CNNs) combining Squeeze and Excitation–based Residual Networks with the next dimension (SE-ResNeXT) and Long Short-Term Memory (LSTM) Networks in order to address this issue. This research work primarily used data from the Radiological Society of North America (RSNA) brain CT hemorrhage challenge dataset and the CQ500 dataset. Preprocessing and data augmentation are performed using the windowing technique in the proposed work. The ICH is then classified using ensembled CNN techniques after being preprocessed, followed by feature extraction in an automatic manner. ICH is classified into the following five types: epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural. A gradient-weighted Class Activation Mapping method (Grad-CAM) is used for identifying the region of interest in an ICH image. A number of performance measures are used to compare the experimental results with various state-of-the-art algorithms. By achieving 99.79% accuracy with an F-score of 0.97, the proposed model proved its efficacy in detecting ICH compared to other deep learning models. The proposed ensembled model can classify epidural, intraventricular, subarachnoid, intra-parenchymal, and subdural hemorrhages with an accuracy of 99.89%, 99.65%, 98%, 99.75%, and 99.88%. Simulation results indicate that the suggested approach can categorize a variety of intracranial bleeding types. By implementing the ensemble deep learning technique using the SE-ResNeXT and LSTM models, we achieved significant classification accuracy and AUC scores.

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“…Table 5 provides a brief result analysis of the AICH-FDLSI with recent techniques. A brief sens y analysis of the AICH-FDLSI technique with existing approaches [ 16 , 24 – 27 ] is provided in Figure 10 . The figure shows that the UNet, WANN, and SVM techniques have attained lower sens y values of 63.10%, 60.18%, and 76.38%, respectively.…”

Section: Performance Validationmentioning

confidence: 99%

Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis

Alfaer

Aljohani

Abdel‐Khalek

et al. 2022

Journal of Healthcare Engineering

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Natural computing refers to computational processes observed in nature and human-designed computing inspired by nature. In recent times, data fusion in the healthcare sector becomes a challenging issue, and it needs to be resolved. At the same time, intracerebral haemorrhage (ICH) is the injury of blood vessels on the brain cells, which is mainly liable for stroke. X-rays and computed tomography (CT) scans are widely applied for locating the haemorrhage position and size. Since manual segmentation of the CT scans by planimetry by the use of radiologists is a time-consuming process, deep learning (DL) is used to attain effective ICH diagnosis performance. This paper presents an automated intracerebral haemorrhage diagnosis using fusion-based deep learning with swarm intelligence (AICH-FDLSI) algorithm. The AICH-FDLSI model operates on four major stages namely preprocessing, image segmentation, feature extraction, and classification. To begin with, the input image is preprocessed using the median filtering (MF) technique to remove the noise present in the image. Next, the seagull optimization algorithm (SOA) with Otsu multilevel thresholding is employed for image segmentation. In addition, the fusion-based feature extraction model using the Capsule Network (CapsNet) and EfficientNet is applied to extract a useful set of features. Moreover, deer hunting optimization (DHO) algorithm is utilized for the hyperparameter optimization of the CapsNet and DenseNet models. Finally, a fuzzy support vector machine (FSVM) is applied as a classification technique to identify the different classes of ICH. A set of simulations takes place to determine the diagnostic performance of the AICH-FDLSI model using the benchmark intracranial haemorrhage data set. The experimental outcome stated that the AICH-FDLSI model has reached a proficient performance over the compared methods in a significant way.

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Machine Learning Model for Intracranial Hemorrhage Diagnosis and Classification

Cited by 17 publications

References 26 publications

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

An automated system for classifying types of cerebral hemorrhage based on image processing techniques

Automated Computer-Aided Detection and Classification of Intracranial Hemorrhage Using Ensemble Deep Learning Techniques

Fusion-Based Deep Learning with Nature-Inspired Algorithm for Intracerebral Haemorrhage Diagnosis

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