Multi-Digit Recognition using Image Processing and Neural Network

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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Real-Time Greenhouse Environmental Conditions Optimization Using Neural Network and Image Processing

2020 20th International Conference on Advances in ICT for Emerging Regions (ICTer) ◽

10.1109/icter51097.2020.9325472 ◽

2020 ◽

Author(s):

Piyumi Wickramaarachchi ◽

Niroshan Balasooriya ◽

Lakmal Welipenne ◽

Sachintha Gunasekara ◽

Anuradha Jayakody

Keyword(s):

Neural Network ◽

Image Processing ◽

Real Time ◽

Environmental Conditions

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Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽

10.3390/e23070816 ◽

2021 ◽

Vol 23 (7) ◽

pp. 816

Author(s):

Pingping Liu ◽

Xiaokang Yang ◽

Baixin Jin ◽

Qiuzhan Zhou

Keyword(s):

Neural Network ◽

Image Processing ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Image Classification ◽

Network Model ◽

Rapid Development ◽

Image Data ◽

Lesion Detection ◽

Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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Automated Classification of Alzheimer’s Disease Based on MRI Image Processing using Convolutional Neural Network (CNN) with AlexNet Architecture

Journal of Physics Conference Series ◽

10.1088/1742-6596/1844/1/012020 ◽

2021 ◽

Vol 1844 (1) ◽

pp. 012020

Author(s):

Y N Fu’adah ◽

I Wijayanto ◽

N K C Pratiwi ◽

F F Taliningsih ◽

S Rizal ◽

...

Keyword(s):

Neural Network ◽

Alzheimer’S Disease ◽

Image Processing ◽

Alzheimer's Disease ◽

Convolutional Neural Network ◽

Automated Classification ◽

Mri Image

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FPGA acceleration on a multi-layer perceptron neural network for digit recognition

The Journal of Supercomputing ◽

10.1007/s11227-021-03849-7 ◽

2021 ◽

Author(s):

Isaac Westby ◽

Xiaokun Yang ◽

Tao Liu ◽

Hailu Xu

Keyword(s):

Neural Network ◽

Multi Layer Perceptron ◽

Digit Recognition ◽

Fpga Acceleration

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Analysis of the Nosema Cells Identification for Microscopic Images

Sensors ◽

10.3390/s21093068 ◽

2021 ◽

Vol 21 (9) ◽

pp. 3068

Author(s):

Soumaya Dghim ◽

Carlos M. Travieso-González ◽

Radim Burget

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Processing ◽

Deep Learning ◽

The Other ◽

Support Vector ◽

Learning Approaches ◽

Microscopic Images ◽

Trained Neural Network ◽

Nosema Disease

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

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