Attribute-based knowledge transfer learning for human pose estimation

Li, Feng; Zhou, Shuren; Zhang, Jianming; Zhang, Deng‐Yong; Xiang, Lingyun

doi:10.1016/j.neucom.2012.05.032

Cited by 14 publications

(9 citation statements)

References 23 publications

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“…The threshold value of a skull or bone MRI image will be greater than 200 compared to the tumor or other portion of the brain, and considering the threshold of the image being filtered. The skull picture is derived using the notion of solidity [20].…”

Section: Skull Strippingmentioning

confidence: 99%

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Jayanthi¹,

Kavitha²,

Jayasankar³

et al. 2021

Computers, Materials &Amp; Continua

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Image recognition is considered to be the pre-eminent paradigm for the automatic detection of tumor diseases in this era. Among various cancers identified so far, glioma, a type of brain tumor, is one of the deadliest cancers, and it remains challenging to the medicinal world. The only consoling factor is that the survival rate of the patient is increased by remarkable percentage with the early diagnosis of the disease. Early diagnosis is attempted to be accomplished with the changes observed in the images of suspected parts of the brain captured in specific interval of time. From the captured image, the affected part of the brain is analyzed using magnetic resonance imaging (MRI) technique. Existence of different modalities in the captured MRI image demands the best automated model for the easy identification of malignant cells. Number of image processing techniques are available for processing the images to identify the affected area. This study concentrates and proposes to improve early diagnosis of glioma using a preprocessing boosted teaching and learning optimization (P-BTLBO) algorithm that automatically segments a brain tumor in an given MRI image. Preprocessing involves contrast enhancement and skull stripping procedures through contrast limited adaptive histogram equalization technique. The traditional TLBO algorithm that works with the perspective of teacher and the student is here improved by using a boosting mechanism. The results obtained using this P-BTLBO algorithm is compared on different benchmark images for the validation of its This work is licensed under a Creative Commons Attribution 4.

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Section: Skull Strippingmentioning

confidence: 99%

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Jayanthi¹,

Kavitha²,

Jayasankar³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…This is done to reduce the number of incorrect detection made by the proposed scheme as the weight assigned to different input sources of detection will be eliminating the outliers. This phase considered two cases: (1) video (human pose analysis [24], person and visual identification [25][26][27], action recognition [28]) and (2) audio data collected from sensors (linguistic analysis [29,30]). The output of this phase is obtained on the basis of the following function:…”

Section: Phase 2: Ensemble Approach For Abnormality Trackingmentioning

confidence: 99%

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Sharma

Dudeja

Aujla

et al. 2020

Neural Comput & Applic

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Healthcare 4.0 paradigm aims at realization of data-driven and patient-centric health systems wherein advanced sensors can be deployed to provide personalized assistance. Hence, extreme mentally affected patients from diseases like Alzheimer can be assisted using sophisticated algorithms and enabling technologies. Motivated from this fact, in this paper, DeTrAs: Deep Learning-based Internet of Health Framework for the Assistance of Alzheimer Patients is proposed. DeTrAs works in three phases: (1) A recurrent neural network-based Alzheimer prediction scheme is proposed which uses sensory movement data, (2) an ensemble approach for abnormality tracking for Alzheimer patients is designed which comprises two parts: (a) convolutional neural network-based emotion detection scheme and (b) timestamp window-based natural language processing scheme, and (3) an IoT-based assistance mechanism for the Alzheimer patients is also presented. The evaluation of DeTrAs depicts almost 10–20% improvement in terms of accuracy in contrast to the different existing machine learning algorithms.

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“…Accuracy is the fundamental feature of recommender systems they ought to limit false‐positive and false‐negative errors. The recent advancements have supported the adoption of artificial intelligence and machine learning methods for proficient expectations, 14‐17 which help therapeutic specialists in settling to a reasonable choice of treatment for various contamination's at a starting time. The methodology of ailment investigation depends strongly on hypochondriac and clinical test data.…”

Section: Related Workmentioning

confidence: 99%

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

Sharma

Aujla

Bajaj

2020

Trans Emerging Tel Tech

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Internet of Things (IoT) and Data science have revolutionized the entire technological landscape across the globe. Because of it, the health care ecosystems are adopting the cutting‐edge technologies to provide assistive and personalized care to the patients. But, this vision is incomplete without the adoption of data‐focused mechanisms (like machine learning, big data analytics) that can act as enablers to provide early detection and treatment of patients even without admission in the hospitals. Recently, there has been an increasing trend of providing assistive recommendation and timely alerts regarding the severity of the disease to the patients. Even, remote monitoring of the present day health situation of the patient is possible these days though the analysis of the data generated using IoT devices by doctors. Motivated from these facts, we design a health care recommendation system that provides a multilevel decision‐making related to the risk and severity of the patient diseases. The proposed systems use an all‐disease classification mechanism based on convolutional neural networks to segregate different diseases on the basis of the vital parameters of a patient. After classification, a fuzzy inference system is used to compute the risk levels for the patients. In the last step, based on the information provided by the risk analysis, the patients are provided with the potential recommendation about the severity staging of the associated diseases for timely and suitable treatment. The proposed work has been evaluated using different datasets related to the diseases and the outcomes seem to be promising.

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Attribute-based knowledge transfer learning for human pose estimation

Cited by 14 publications

References 23 publications

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

Segmentation of Brain Tumor Magnetic Resonance Images Using a Teaching-Learning Optimization Algorithm

DeTrAs: deep learning-based healthcare framework for IoT-based assistance of Alzheimer patients

Deep neuro‐fuzzy approach for risk and severity prediction using recommendation systems in connected health care

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