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

Deepa, B.; Murugappan, M.; Sumithra, M. G.; Mahmud, Mufti; Al-Rakhami, Mabrook

doi:10.1109/access.2021.3100549

Cited by 47 publications

(7 citation statements)

References 42 publications

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“…A hybridised support cornerstone intended desultory growth classifier is utilized to classify data using the suggested method, which also comprises feature extraction based on fineness, earnestness, and silhouette, character filtering, and categorizing. Utilising a variety of performance indicators, the suggested method achieves a consistent accuracy for pronouncement case history of brain tumours and cataloguing essential facts of intent stroke [24]. The suggested strategy successfully divides cases of acute and sub-acute strokes as well as lofty-calibre and shallow-calibre tumours in brain.…”

Section: Literature Surveymentioning

confidence: 99%

A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet

Sundari.M,

Divya,

Durga

et al. 2024

IJCDS

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Brain tumors can be a life-threatening condition, and early detection is crucial for effective treatment. Magnetic resonance imaging (MRI) is a valuable appliance for identifying the tumor's location, but manual detection is a time-engrossing and flaws-prone process. To overcome these challenges, computer-assisted approaches have been developed, and deep learning (DL) archetypes are now being pre-owned in medical imaging to discover brain tumors maneuver MRI carbon copies. In this, we propose a deep convolutional neural network (CNN) Xception net model for the efficient classification and detection of brain tumor images. We utilized the "Br35H :: Brain Tumor Detection 2020" dataset sourced from Kaggle, which encompasses 3000 MRI images of brain tumors, each with a file size of 88 megabytes. The Xception net is a powerful CNN model that has shown promising results in various systems perceiving exercise, in conjunction with medical illustration scrutiny. We fine-tuned the Xception net model using a dataset of Magnetic Resonance Imaging (MRI) images of the brain, which were pre-processed and labeled by medical experts. To reckon the performance of our prototype, we counselled dossier using a variety of interpretation criterion, including accuracy, precision, recall, and F1 score. Our customs view that the urged model achieved high accuracy in classifying brain tumor images. The archetype's strength to accurately and efficiently classify and detect brain tumors using MRI images can significantly improve patient outcomes by enabling early detection and treatment. Overall, our study demonstrates the persuasiveness of using the Xception net flawless for brain tumor ferreting out and alloting using MRI images with 94% of accuracy performance. The proposed model has the potential to revolutionize the department of salutary exemplify and improve patient outcomes for brain tumor treatment.

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Section: Literature Surveymentioning

confidence: 99%

A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet

Sundari.M,

Divya,

Durga

et al. 2024

IJCDS

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“…The use of deep learning for medical image analysis has risen dramatically in recent years [ 11 , 12 , 13 , 14 ]. Automatic classification and detection of acute ICH using deep learning algorithms is presented in [ 15 ].…”

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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“…Diagnosis of DR can be performed through either manual examination by an ophthalmologist or by utilising an automated system. With the advancements in Artificial Intelligence (AI) techniques, automated system development has been facilitated in many application areas including anomaly detection [ 3 ], brain signal analysis [ 4 ], neurodevelopmental disorder assessment and classification focusing on autism [ 5 , 6 , 7 ], neurological disorder detection and management [ 8 ], supporting the detection and management of the COVID-19 pandemic [ 9 ], cyber security and trust management [ 10 , 11 , 12 , 13 ], various disease diagnosis [ 14 , 15 , 16 , 17 ], smart healthcare service delivery [ 18 , 19 ], text and social media mining [ 20 , 21 ], understanding student engagement [ 22 , 23 ], etc. As can be seen in the literature, automated systems for early disease detection have been a major area of development.…”

Section: Introductionmentioning

confidence: 99%

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

Sundar,

Subramanian,

Mahmud

2024

Diagnostics

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Diabetic retinopathy (DR) arises from blood vessel damage and is a leading cause of blindness on a global scale. Clinical professionals rely on examining fundus images to diagnose the disease, but this process is frequently prone to errors and is tedious. The usage of computer-assisted techniques offers assistance to clinicians in detecting the severity levels of the disease. Experiments involving automated diagnosis employing convolutional neural networks (CNNs) have produced impressive outcomes in medical imaging. At the same time, retinal image grading for detecting DR severity levels has predominantly focused on spatial features. More spectral features must be explored for a more efficient performance of this task. Analysing spectral features plays a vital role in various tasks, including identifying specific objects or materials, anomaly detection, and differentiation between different classes or categories within an image. In this context, a model incorporating Wavelet CNN and Support Vector Machine has been introduced and assessed to classify clinically significant grades of DR from retinal fundus images. The experiments were conducted on the EyePACS dataset and the performance of the proposed model was evaluated on the following metrics: precision, recall, F1-score, accuracy, and AUC score. The results obtained demonstrate better performance compared to other state-of-the-art techniques.

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

Cited by 47 publications

References 42 publications

A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet

A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet

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

Classification of Diabetic Retinopathy Disease Levels by Extracting Spectral Features Using Wavelet CNN

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