An IoT platform for the analysis of brain CT images based on Parzen analysis

Sarmento, Róger Moura; Vasconcelos, Francisco Fábio Ximenes; Filho, Pedro Pedrosa Rebouças; Albuquerque, Victor Hugo C. de

doi:10.1016/j.future.2019.11.033

Cited by 23 publications

(14 citation statements)

References 40 publications

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“…For future studies, We planned to be expanded with the IoT system, as in similar healthcare-studies in the literature [28]- [30]. In this way, while the patients can be diagnosed remotely, the specialists' increasing workload will be reduced due to the increasing COVID-19 cases.…”

Section: Discussionmentioning

confidence: 99%

Derin Öğrenme Yöntemleri ile COVID-19 Teşhisi

Güraksın

Barin

Özgül

et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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The coronavirus, which appeared in Wuhan city of China and named COVID-19 , spread rapidly and caused the death of many people. Early diagnosis is very important to prevent or slow the spread. The first preferred method by clinicians is real-time reverse transcription-polymerase chain reaction (RT-PCR). However, expected accuracy values cannot be obtained in the diagnosis of patients in the incubation period. Therefore, common lung devastation in COVID-19 patients were considered and radiological lung images were used to diagnose. In this study, automatic COVID-19 diagnosis was made from posteroanterior (PA) chest X-Ray images by deep learning method. In the study, using two different deep learning methods, classification was made with different dataset combinations consisting of healthy, COVID, bacterial pneumonia and viral pneumonia X-ray images. The results show that the proposed deep learning-based system can be used in the clinical setting as a supplement to RT-PCR test for early diagnosis

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Section: Discussionmentioning

confidence: 99%

Derin Öğrenme Yöntemleri ile COVID-19 Teşhisi

Güraksın

Barin

Özgül

et al. 2021

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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“…De Albuquerque et al (2017) investigate the applications of brain computer interface systems. Some IoT frameworks are proposed to analyze the brain signals, such as brain CT images (Jaiswal et al, 2019;Sarmento et al, 2020;Vasconcelos et al, 2020), MRI (Mallick et al, 2019;Arunkumar et al, 2020), etc. Many applications benefit human daily life.…”

Section: Related Workmentioning

confidence: 99%

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

Zhu

Zeng

Wang

et al. 2020

Front. Hum. Neurosci.

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Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species.

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“…The strokes are classified as two types: (1) hemorrhage and (2) ischemic. Here, acute ischemic is occurred by the block of blood vessels, whereas, acute hemorrhage is occurred by the break of blood vessel 3,4 . This stroke is mainly attacking due to the failure of heart (atherosclerosis, cardiac arrhythmias, cardiac insufficiency) 5,6 .…”

Section: Introductionmentioning

confidence: 99%

“…Here, acute ischemic is occurred by the block of blood vessels, whereas, acute hemorrhage is occurred by the break of blood vessel. 3,4 This stroke is mainly attacking due to the failure of heart (atherosclerosis, cardiac arrhythmias, cardiac insufficiency). 5,6 If the disease is detected early, the disease can be cured.…”

Section: Introductionmentioning

confidence: 99%

A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification

Melingi¹,

Mojjada²,

Reddy³

et al. 2022

Concurrency and Computation

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In this article, a bio-inspired AlexNet-DrpXLm architype is proposed for an effective brain stroke lesion detection and classification within a short period. Here, the input CT image datasets are collected from Himalayan Institute of Medical Sciences, then the images are preprocessed to take away the noises and also enhance the quality of the images. After that, the input images are trained and the features are extracted with the help of AlexNet model, and then classified as the brain images of normal and abnormal. The last three layers of the AlexNet model are replaced with the dropout extreme learning machine (DrpXLM) classifier to classify the images in efficient manner. The DrpXLM weight parameters are tuned by improved dolphin swarm optimization algorithm. The proposed method attains higher accuracy 99.54%, high precision 90.43%, high F-score 89.40%, higher specificity 93.56%, higher sensitivity 93.56%, lower computational time 0.02 s, and the proposed method is compared to the existing methods, like random decision forest with gravitational search algorithm, hybrid native Bayes and sample weighted with random forest classification algorithm, and random forest with fractional-order Darwinian particle swarm optimization.

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An IoT platform for the analysis of brain CT images based on Parzen analysis

Cited by 23 publications

References 40 publications

Derin Öğrenme Yöntemleri ile COVID-19 Teşhisi

Derin Öğrenme Yöntemleri ile COVID-19 Teşhisi

Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model

A bio‐inspired AlexNet‐DrpXLm architype for an effective brain stroke lesion detection and classification

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