http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
- 中文 을 입력하시려면 zhongwen을 입력하시고 space를누르시면됩니다.
- 北京 을 입력하시려면 beijing을 입력하시고 space를 누르시면 됩니다.
RISS 인기검색어
- 검색키워드
- 저자 : R. Menaka (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
-
R. Kanchana,R. Menaka 대한의용생체공학회 2020 Biomedical Engineering Letters (BMEL) Vol.10 No.3
Ischemic stroke is the dominant disorder for mortality and morbidity. For immediate diagnosis and treatment plan of ischemicstroke, computed tomography (CT) images are used. This paper proposes a histogram bin based novel algorithm to segmentthe ischemic stroke lesion using CT and optimal feature group selection to classify normal and abnormal regions. Stepsfollowed are pre-processing, segmentation, extracting texture features, feature ranking, feature grouping, classifi cation andoptimal feature group (FG) selection. The fi rst order features, gray level run length matrix features, gray level co-occurrencematrix features and Hu’s moment features are extracted. Classifi cation is done using logistic regression (LR), support vectormachine classifi er (SVMC), random forest classifi er (RFC) and neural network classifi er (NNC). This proposed approacheff ectively detects ischemic stroke lesion with a classifi cation accuracy of 88.77%, 97.86%, 99.79% and 99.79% obtained bythe LR, SVMC, RFC and NNC when FG12 is opted, which is validated by fourfold cross validation.
-
Delineation of ischemic lesion from brain MRI using attention gated fully convolutional network
R. Karthik,Menaka Radhakrishnan,R. Rajalakshmi,Joel Raymann 대한의용생체공학회 2021 Biomedical Engineering Letters (BMEL) Vol.11 No.1
Precise delineation of the ischemic lesion from unimodal Magnetic Resonance Imaging (MRI) is a challenging task due tothe subtle intensity difference between the lesion and normal tissues. Hence, multispectral MRI modalities are used for characterizingthe properties of brain tissues. Traditional lesion detection methods rely on extracting significant hand-engineeredfeatures to differentiate normal and abnormal brain tissues. But the identification of those discriminating features is quitecomplex, as the degree of differentiation varies according to each modality. This can be addressed well by ConvolutionalNeural Networks (CNN) which supports automatic feature extraction. It is capable of learning the global features from imageseffectively for image classification. But it loses the context of local information among the pixels that need to be retained forsegmentation. Also, it must provide more emphasis on the features of the lesion region for precise reconstruction. The majorcontribution of this work is the integration of attention mechanism with a Fully Convolutional Network (FCN) to segmentischemic lesion. This attention model is applied to learn and concentrate only on salient features of the lesion region bysuppressing the details of other regions. Hence the proposed FCN with attention mechanism was able to segment ischemiclesion of varying size and shape. To study the effectiveness of attention mechanism, various experiments were carried outon ISLES 2015 dataset and a mean dice coefficient of 0.7535 was obtained. Experimental results indicate that there is animprovement of 5% compared to the existing works.
-
( Ramya Sivanesan ),( Alvia Anwar ),( Abhishek Talwar ),( Menaka. R ),( Karthik. R ) 한국인터넷정보학회 2016 KSII Transactions on Internet and Information Syst Vol.10 No.9
With millions of people across the globe suffering from Parkinson`s disease (PD), an objective, confirmatory test for the same is yet to be developed. This research aims to develop a system which can assist the doctor in objectively saying whether the patient is normal or under risk of PD. The proposed work combines the eye-hand co-ordination behaviour with the DaTscan images in order to determine the risk of this disorder. Initially, eye-hand coordination level of the patient is assessed through a hardware module. Then, the DaTscan image is analysed and used to extract certain geometrical parameters which shall indicate the presence of PD. These parameters are then finally fed into a Multi-Layer Perceptron Neural Network using Levenberg-Marquardt (LM) Back propagation training algorithm. Experimental results indicate that the proposed system exhibits an accuracy of around 93%.
ScienceON
KISS연관 검색어 추천
이 검색어로 많이 본 자료
활용도 높은 자료
