Brain disease diagnosis using local binary pattern and steerable pyramid

Kale, Vandana V.; Hamde, Satish T.; Holambe, Raghunath S.

doi:10.1007/s13735-019-00174-x

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…Classification is a popular technique in the field of biomedical condition monitoring and detection purposes. In few recent studies, the classification technique is used for brain disease [18,24] and tumour detection from medical image data [23,36], analysis of chest disease [19], electromyogram (EMG) signal classification [17], pneumonia disease classification [14], detection of epileptic seizure from EEG signals [21], arrhythmia detection [25], even detection of Covid-19 from x-ray [3] to name a few.…”

Section: Introductionmentioning

confidence: 99%

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Sarkar

Goswami

Ghosh

2021

Preprint

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Hypertension or high blood pressure is a severe health issue in the modern world, especially in this pandemic scenario, that can cause many heart related diseases or even death, and it is increasing day by day. For this reason, a reliable, automatic and easy to use system for hypertensive subject detection is an important focus for the researchers. Biopotential signals can play a pivotal role in this regard. Though, few strategies were proposed based on electrocardiogram (ECG) or electrodermal (EDA) signals, but those require special circuitry, as well as trained persons. In this article, a method is proposed to classify hypertensive and normotensive subjects using differential biopotential signals. Neither special circuitry, nor much expertise is required for handling this system. It was assumed that progression of rest is dependent upon blood pressure. To serve the purpose, signals were acquired from both hypertensive and normotensive subjects bilaterally for 10 continuous minutes. Result of the random forest (RF) classification establishes that from the analysis of the progression of the bilaterally acquired differential biopotential signals, hypertensive subjects can be distinguished from normotensive subjects.

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Section: Introductionmentioning

confidence: 99%

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Sarkar

Goswami

Ghosh

2021

Preprint

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Rescuing Relevant Features from Active Aging Surveys: A Data Mining Perspective

Lima

Cedillo

Acosta-Urigüen

et al. 2023

Communications in Computer and Information Science

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Comparing of brain tumor diagnosis with developed local binary patterns methods

Gül,

Kaya

2024

Neural Comput & Applic

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A brain tumor is one of the most lethal diseases that can affect human health and cause death. Invasive biopsy techniques are one of the most common methods of identifying brain tumor disease. As a result of this procedure, bleeding may occur during the procedure, which could harm some brain functions. Consequently, this invasive biopsy process may be extremely dangerous. To overcome such a dangerous process, medical imaging techniques, which can be used by experts in the field, can be used to conduct a thorough examination and obtain detailed information about the type and stage of the disease. Within the scope of the study, the dataset was examined, and this dataset consisted of brain images with tumors and brain images of normal patients. Numerous studies on medical images were conducted and obtained with high accuracy within the hybrid model algorithms. The dataset's images were enhanced using three distinct local binary patterns (LBP) algorithms in the developed model within the scope of the study: the LBP, step-LBP (nLBP), and angle-LBP (αLBP) algorithms. In the second stage, classification algorithms were used to evaluate the results from the LBP, nLBP and αLBP algorithms. Among the 11 classification algorithms used, four different classification algorithms were chosen as a consequence of the experimental process since they produced the best results. The classification algorithms with the best outcomes are random forest (RF), optimized forest (OF), rotation forest (RF), and instance-based learner (IBk) algorithms, respectively. With the developed model, an extremely high success rate of 99.12% was achieved within the IBk algorithm. Consequently, the clinical service can use the developed method to diagnose tumor-based medical images.

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Brain disease diagnosis using local binary pattern and steerable pyramid

Cited by 7 publications

References 24 publications

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Classification of Hypertensive and Normotensive Subjects Using Bilateral Differential Biopotential Signals

Rescuing Relevant Features from Active Aging Surveys: A Data Mining Perspective

Comparing of brain tumor diagnosis with developed local binary patterns methods

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