scholarly journals HandSegNet: Hand segmentation using convolutional neural network for contactless palmprint recognition

2021 ◽  
Author(s):  
Koichi Ito ◽  
Yusei Suzuki ◽  
Hiroya Kawai ◽  
Takafumi Aoki ◽  
Masakazu Fujio ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Palmprint Recognition ◽  
Hand Segmentation

scholarly journals Genetic Algorithm Based Gabor CNN For Palmprint Recognition

2020 ◽  
Vol 8 (6) ◽  
pp. 4895-4899
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Convolutional Neural Network ◽  
Gabor Filter ◽  
Fitness Function ◽  
Palmprint Recognition ◽  
Central Frequency ◽  
The Past ◽  
Tremendous Progress ◽  
2D Gabor Filter

In the field of biometrics, palmprint recognition has received great interest and made tremendous progress in the past two decades. In palmprint recognition, the important step is to extract the discriminative features from the image and compare it with templates for identification and verification tasks. In this paper, a new genetic-based 2D Gabor filter with the Convolutional Neural Network is presented. The scale and orientation details captured by Gabor filters are optimized based on central frequency, which is determined based on genetic algorithm fitness function. The proposed technique is implemented on four publicly available palmprint datasets- PolyU, CASIA, IITD, and Tongji. Experimental results confirm that the proposed technique achieves better accuracy when compared to Palmnet.

scholarly journals A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia

2022 ◽  
Vol 25 (1) ◽  
pp. 200
Author(s):  
Shakir Mahmood Abas ◽  
Adnan Mohsin Abdulazeez ◽  
Diyar Qader Zeebaree
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Lymphoblastic Leukemia ◽  
White Blood Cells ◽  
Complete Information ◽  
Computer Aided Detection ◽  
Hand Segmentation ◽  
Computer Aided

The developing of deep learning systems that used for chronic diseases diagnosing is challenge. Furthermore, the localization and identification of objects like white blood cells (WBCs) in leukemia without preprocessing or traditional hand segmentation of cells is a challenging matter due to irregular and distorted of nucleus. This paper proposed a system for computer-aided detection depend completely on deep learning with three models computer-aided detection (CAD3) to detect and classify three types of WBC which is fundamentals of leukemia diagnosing. The system used modified you only look once (YOLO v2) algorithm and convolutional neural network (CNN). The proposed system trained and evaluated on dataset created and prepared specially for the addressed problem without any traditional segmentation or preprocessing on microscopic images. The study proved that dividing of addressed problem into sub-problems will achieve better performance and accuracy. Furthermore, the results show that the CAD3 achieved an average precision (AP) up to 96% in the detection of leukocytes and accuracy 94.3% in leukocytes classification. Moreover, the CAD3 gives report contain a complete information of WBC. Finally, the CAD3 proved its efficiency on the other dataset such as acute lymphoblastic leukemia image database (ALL-IBD1) and blood cell count dataset (BCCD).

CONVOLUTIONAL NEURAL NETWORK BASED ALGORITHM FOR CECUM ACHIEVEMENT CONFIRMATION

2020 ◽  
Author(s):  
S Kashin ◽  
D Zavyalov ◽  
A Rusakov ◽  
V Khryashchev ◽  
A Lebedev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network

Non-Blind Image Deconvolution Based on “Ringing” Removal Using Convolutional Neural Network

2020 ◽  
Vol 2020 (10) ◽  
pp. 181-1-181-7
Author(s):  
Takahiro Kudo ◽  
Takanori Fujisawa ◽  
Takuro Yamaguchi ◽  
Masaaki Ikehara
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Large Scale ◽  
Blind Deconvolution ◽  
Training Dataset ◽  
Image Deconvolution ◽  
Classic Problem ◽  
Key Points ◽  
Blind Image

Image deconvolution has been an important issue recently. It has two kinds of approaches: non-blind and blind. Non-blind deconvolution is a classic problem of image deblurring, which assumes that the PSF is known and does not change universally in space. Recently, Convolutional Neural Network (CNN) has been used for non-blind deconvolution. Though CNNs can deal with complex changes for unknown images, some CNN-based conventional methods can only handle small PSFs and does not consider the use of large PSFs in the real world. In this paper we propose a non-blind deconvolution framework based on a CNN that can remove large scale ringing in a deblurred image. Our method has three key points. The first is that our network architecture is able to preserve both large and small features in the image. The second is that the training dataset is created to preserve the details. The third is that we extend the images to minimize the effects of large ringing on the image borders. In our experiments, we used three kinds of large PSFs and were able to observe high-precision results from our method both quantitatively and qualitatively.

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