A Convolution Neural Network Engine for Sclera Recognition

Maheshan, M. S.; Harish, B. S.; Nagadarshan, N.

doi:10.9781/ijimai.2019.03.006

Cited by 12 publications

(11 citation statements)

References 27 publications

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“…All this work confirms that the extraction and evaluation of the WBC is a significant task during the blood level disease detection and to reduce the diagnostic burden, it is necessary to employ an automated WBC evaluation system. The recent works in the literature confirms that the CNN approaches help to achieve a superior result during the data assessment [32][33][34][35]. Hence, this research aims in implementing the CNN supported scheme to assess the considered database.…”

Section: Related Workmentioning

confidence: 88%

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

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Medical images play a fundamental role in disease screening, and automated evaluation of these images is widely preferred in hospitals. Recently, Convolutional Neural Network (CNN) supported medical data assessment is widely adopted to inspect a set of medical imaging modalities. Extraction of the leukocyte section from a thin blood smear image is one of the essential procedures during the preliminary disease screening process. The conventional segmentation needs complex/hybrid procedures to extract the necessary section and the results achieved with conventional methods sometime tender poor results. Hence, this research aims to implement the CNN-assisted image segmentation scheme to extract the leukocyte section from the RGB scaled hematological images. The proposed work employs various CNN-based segmentation schemes, such as SegNet, U-Net, and VGG-UNet. We used the images from the Leukocyte Images for Segmentation and Classification (LISC) database. In this work, five classes of the leukocytes are considered, and each CNN segmentation scheme is separately implemented and evaluated with the ground-truth image. The experimental outcome of the proposed work confirms that the overall results accomplished with the VGG-UNet are better (Jaccard-Index = 91.5124%, Dice-Coefficient = 94.4080%, and Accuracy = 97.7316%) than those of the SegNet and U-Net schemes Finally, the merit of the proposed scheme is also confirmed using other similar image datasets, such as Blood Cell Count and Detection (BCCD) database and ALL-IDB2. The attained result confirms that the proposed scheme works well on hematological images and offers better performance measure values.

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Section: Related Workmentioning

confidence: 88%

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Kadry¹,

Rajinikanth

Taniar

et al. 2021

J Supercomput

View full text Add to dashboard Cite

show abstract

“…Some deep learning approaches like CNN allow classification directly on raw data without the using of features extraction (Maheshan et al, 2020; Wang et al, 2019). The CNN model performs the three important work, that is, sparse interaction, parameter sharing, and equivalent representation and after convolution, there are used pooling and fully connected layers for classification and regression tasks (Zhao, Zheng, et al, 2019; Zhu et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In Caon et al (2011) and , two perpendicular sensors were used to track the activity, due to lagging if one sensor does not track the joint, coordinates of un-tracked joint is to be substituted by the readings of another Kinect sensor recordings. Some deep learning approaches like CNN allow classification directly on raw data without the using of features extraction (Maheshan et al, 2020;Wang et al, 2019). The CNN model performs the three important work, that is, sparse interaction, parameter sharing, and equivalent representation and after convolution, there are used pooling and fully connected layers for classification and regression tasks (Zhao, Zheng, et al, 2019;Zhu et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Heterogeneous computing model for post‐injury walking pattern restoration and postural stability rehabilitation exercise recognition

Bijalwan¹,

Semwal

Singh

et al. 2021

Expert Systems

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The research paper presents the heterogeneous computing model for analysis & restoration of human walking deformity and posture instability. Gait‐related walking activities are very important for the analysis of postural instability, repairment of gait abnormality, diagnosis of cognitive declination, enhance the cognitive ability of human‐centered humanoid robot system, and many clinical diagnoses, for example, Parkinson, pathological gait, freezing of gait, etc. at an early stage. For experiment analysis, 10 different lower limb activities are being considered of healthy and crouch walking subjects. A total of 25 healthy and 10 crouch walk subjects are considered for experiment purposes of different age groups, sex, and mental status. To achieve this objective the pattern of 10 different rehabilitation activities are captured using RGB‐Depth (RGB‐D) camera and classified using heterogeneous deep learning models. Different deep learning models Convolutional Neural Network (CNN) and CNN‐LSTM (CNN‐Long Short Term Memory) are used for the classification of these rehabilitation exercises. The RGB‐D data is obtained using a Microsoft Kinect v2 sensor on a 100 Hz sampling frequency. Experimental results have shown significant activity recognition accuracy with 96% and 98% for CNN and CNN‐LSTM models respectively.

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“…MCTS has been successfully applied in different contexts. The most important milestone since its appearance is perhaps the victory of AlphaGo against a Go champion 5-0 by selecting new moves using neural networks ( [16,17]) that were trained with supervised learning [18]. Shortly after, AlphaGo Zero won 100-0 against AlphaGo by selecting new moves using neural networks that were trained with reinforcement learning, without human data [19].…”

Section: Introductionmentioning

confidence: 99%

Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics

et al. 2021

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Monte Carlo Tree Search is one of the main search methods studied presently. It has demonstrated its efficiency in the resolution of many games such as Go or Settlers of Catan and other different problems. There are several optimizations of Monte Carlo, but most of them need heuristics or some domain language at some point, making very difficult its application to other problems. We propose a general and optimized implementation of Monte Carlo Tree Search using neural networks without extra knowledge of the problem. As an example of our proposal, we made use of the Dots and Boxes game. We tested it against other Monte Carlo system which implements specific knowledge for this problem. Our approach improves accuracy, reaching a winning rate of 81% over previous research but the generalization penalizes performance.

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A Convolution Neural Network Engine for Sclera Recognition

Cited by 12 publications

References 27 publications

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Automated segmentation of leukocyte from hematological images—a study using various CNN schemes

Heterogeneous computing model for post‐injury walking pattern restoration and postural stability rehabilitation exercise recognition

Improving Monte Carlo Tree Search with Artificial Neural Networks without Heuristics

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