Pattern Recognition with Convolutional Neural Networks: Humpback Whale Tails

Humpback Whale ◽

And Training

This paper presents a study and implementation of a convolutional neural network to identify and recognize humpback whale specimens from the unique patterns of their tails. Starting from a dataset composed of images of whale tails, all the phases of the process of creation and training of a neural network are detailed – from the analysis and pre-processing of images to the elaboration of predictions, using TensorFlow and Keras frameworks. Other possible alternatives are also explained when it comes to tackling this problem and the complications that have arisen during the process of developing this paper.

Classification of surface water objects in visible spectrum images

Journal of «Almaz – Antey» Air and Defence Corporation ◽

10.38013/2542-0542-2020-1-87-95 ◽

2020 ◽

pp. 87-95

Author(s):

A. A. Artemyev ◽

E. A. Kazachkov ◽

S. N. Matyugin ◽

V. V. Sharonov

Keyword(s):

Neural Network ◽

Neural Networks ◽

Surface Water ◽

Visible Spectrum ◽

Training Dataset ◽

And Training ◽

Selection Of

This paper considers the problem of classifying surface water objects, e.g. ships of different classes, in visible spectrum images using convolutional neural networks. A technique for forming a database of images of surface water objects and a special training dataset for creating a classification are presented. A method for forming and training of a convolutional neural network is described. The dependence of the probability of correct recognition on the number and variants of the selection of specific classes of surface water objects is analysed. The results of recognizing different sets of classes are presented.

SEGMENTATION OF HEMORRHAGE AREAS ON BRAIN CT IMAGES USING CONVOLUTIONAL NEURAL NETWORKS

International scientific journal Internauka ◽

10.25313/2520-2057-2021-8-7398 ◽

2017 ◽

Author(s):

Vladyslav Yurochkin ◽

Keyword(s):

Neural Network ◽

Neural Networks ◽

Ct Images ◽

Brain Ct ◽

And Training

The paper considers the construction of a system for visualization of hemorrhage segmentation on brain CT images by creating and training a convolutional neural network to optimize the procedure for finding pathology in CT diagnostics.

Convolutional Neural Networks for Leaf Image-Based Plant Disease Classification

IAES International Journal of Artificial Intelligence (IJ-AI) ◽

10.11591/ijai.v8.i4.pp328-341 ◽

2019 ◽

Vol 8 (4) ◽

pp. 328

Author(s):

Sachin B. Jadhav

Keyword(s):

Neural Network ◽

Neural Networks ◽

Plant Diseases ◽

Experimental Results ◽

Disease Classification ◽

Soybean Leaves ◽

Soybean Diseases ◽

Validation Strategy

<span lang="EN-US">Plant pathologists desire soft computing technology for accurate and reliable diagnosis of plant diseases. In this study, we propose an efficient soybean disease identification method based on a transfer learning approach by using a pre-trained convolutional neural network (CNN’s) such as AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201. The proposed convolutional neural networks were trained using 1200 plant village image dataset of diseased and healthy soybean leaves, to identify three soybean diseases out of healthy leaves. Pre-trained CNN used to enable a fast and easy system implementation in practice. We used the five-fold cross-validation strategy to analyze the performance of networks. In this study, we used a pre-trained convolutional neural network as feature extractors and classifiers. The experimental results based on the proposed approach using pre-trained AlexNet, GoogleNet, VGG16, ResNet101, and DensNet201 networks achieve an accuracy of 95%, 96.4 %, 96.4 %, 92.1%, 93.6% respectively. The experimental results for the identification of soybean diseases indicated that the proposed networks model achieves the highest accuracy</span>

Document Localization in Images Taken by Smartphones Using a Fully Convolutional Neural Network

10.21203/rs.3.rs-952656/v1 ◽

2021 ◽

Author(s):

Shima Baniadamdizaj ◽

Mohammadreza Soheili ◽

Azadeh Mansouri

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Convolutional Neural Networks ◽

Opposite Hand ◽

Processing Strategies ◽

Document Localization

Abstract Today integration of facts from virtual and paper files may be very vital for the expertise control of efficient. This calls for the record to be localized at the photograph. Several strategies had been proposed to resolve this trouble; however, they may be primarily based totally on conventional photograph processing strategies that aren't sturdy to intense viewpoints and backgrounds. Deep Convolutional Neural Networks (CNNs), on the opposite hand, have demonstrated to be extraordinarily sturdy to versions in history and viewing attitude for item detection and classification responsibilities. We endorse new utilization of Neural Networks (NNs) for the localization trouble as a localization trouble. The proposed technique ought to even localize photos that don't have a very square shape. Also, we used a newly accrued dataset that has extra tough responsibilities internal and is in the direction of a slipshod user. The end result knowledgeable in 3 exclusive classes of photos and our proposed technique has 83% on average. The end result is as compared with the maximum famous record localization strategies and cell applications.

The Role and Utilization of CNN in Automatic Logo Based Document Image Retrieval Methods

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.1.16786 ◽

2018 ◽

Vol 7 (3.1) ◽

pp. 13

Author(s):

Raveendra K ◽

R Vinoth Kanna

Keyword(s):

Neural Network ◽

Neural Networks ◽

Feature Extraction ◽

Image Retrieval ◽

Document Image ◽

Retrieval Process ◽

Pictorial Representations

Automatic logo based document image retrieval process is an essential and mostly used method in the feature extraction applications. In this paper the architecture of Convolutional Neural Network (CNN) was elaborately explained with pictorial representations in order to understand the complex Convolutional Neural Networks process in a simplified way. The main objective of this paper is to effectively utilize the CNN in the process of automatic logo based document image retrieval methods.

A Survey on Techniques used in Medical Imaging Processing

Journal of Physics Conference Series ◽

10.1088/1742-6596/2089/1/012013 ◽

2021 ◽

Vol 2089 (1) ◽

pp. 012013

Author(s):

Priyadarshini Chatterjee ◽

Dutta Sushama Rani

Keyword(s):

Neural Network ◽

Neural Networks ◽

Image Analysis ◽

Network Architecture ◽

Medical Image Analysis ◽

Artificial Neural ◽

Automated Screening ◽

To Come

Abstract Automated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.

Optimasi Akurasi Metode Convolutional Neural Network untuk Identifikasi Jenis Sampah

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ◽

10.29207/resti.v5i2.2754 ◽

2021 ◽

Vol 5 (2) ◽

pp. 312-318

Author(s):

Rima Dias Ramadhani ◽

Afandi Nur Aziz Thohari ◽

Condro Kartiko ◽

Apri Junaidi ◽

Tri Ginanjar Laksana ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Computer Science ◽

No Value ◽

Training Process ◽

Indonesian Government ◽

Speed Up ◽

Model Training

Waste is goods / materials that have no value in the scope of production, where in some cases the waste is disposed of carelessly and can damage the environment. The Indonesian government in 2019 recorded waste reaching 66-67 million tons, which is higher than the previous year, which was 64 million tons. Waste is differentiated based on its type, namely organic and anorganic waste. In the field of computer science, the process of sensing the type waste can be done using a camera and the Convolutional Neural Networks (CNN) method, which is a type of neural network that works by receiving input in the form of images. The input will be trained using CNN architecture so that it will produce output that can recognize the object being inputted. This study optimizes the use of the CNN method to obtain accurate results in identifying types of waste. Optimization is done by adding several hyperparameters to the CNN architecture. By adding hyperparameters, the accuracy value is 91.2%. Meanwhile, if the hyperparameter is not used, the accuracy value is only 67.6%. There are three hyperparameters used to increase the accuracy value of the model. They are dropout, padding, and stride. 20% increase in dropout to increase training overfit. Whereas padding and stride are used to speed up the model training process.

Hand gesture recognition by means of region-based convolutional neural networks

Contemporary Engineering Sciences ◽

10.12988/ces.2017.710154 ◽

2017 ◽

Vol 10 (27) ◽

pp. 1329-1342 ◽

Cited By ~ 2

Author(s):

Javier O. Pinzon Arenas ◽

Robinson Jimenez Moreno ◽

Paula C. Useche Murillo

Keyword(s):

Neural Network ◽

Neural Networks ◽

Real Time ◽

Gesture Recognition ◽

Hand Gesture ◽

Hand Gestures ◽

The Neural Network ◽

Average Accuracy

This paper presents the implementation of a Region-based Convolutional Neural Network focused on the recognition and localization of hand gestures, in this case 2 types of gestures: open and closed hand, in order to achieve the recognition of such gestures in dynamic backgrounds. The neural network is trained and validated, achieving a 99.4% validation accuracy in gesture recognition and a 25% average accuracy in RoI localization, which is then tested in real time, where its operation is verified through times taken for recognition, execution behavior through trained and untrained gestures, and complex backgrounds.

An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

The Journal of Laryngology & Otology ◽

10.1017/s0022215119002536 ◽

2019 ◽

Vol 134 (1) ◽

pp. 52-55 ◽

Cited By ~ 3

Author(s):

J Huang ◽

A-R Habib ◽

D Mendis ◽

J Chong ◽

M Smith ◽

...

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Computed Tomography ◽

Neural Networks ◽

Sinus Surgery ◽

Computed Tomography Scans ◽

Ethmoidal Artery ◽

Anterior Ethmoidal Artery

AbstractObjectiveDeep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.MethodsCoronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.ResultsA total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.ConclusionConvolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.