scholarly journals Butterfly Family Detection and Identification using Convolutional Neural Network for Lepidopterology

2019 ◽  
Vol 8 (2S11) ◽  
pp. 635-640
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Image Search ◽  
Biology Students ◽  
Detection And Identification ◽  
Family Types ◽  
High Level ◽  
Image Format

Lepidopterology is a branch of entomology concerning the scientific study of moths and the three superfamilies of butterflies. The project aims to help biology students in identifying butterfly without harming the insect. In the studies of lepidopterology, the students normally need to capture the butterflies with nets and dissect the insect to identify its family types. Computer vision is a study on how computers can be used to make high-level comprehension from the input of digital image and videos. By utilizing the latest Image Processing technique, it can identify the correct species of butterfly with high accuracy by using layers of node in a Convolutional Neural Network (CNN). The work process starts with data acquisition (mining the butterfly image automatically from google image search), pre-processing (converting image format and rotation), analyzing and understanding digital images (group images into folders), and to make assumptions of the high complication data from the real world in the process of producing numerical information that can be comprehend by machines in order to form conclusions. Benefits of using CNN is to reduce the need for human and physical intervention in identifying each of the butterfly characters. This makes it easier to expand the database in the future. The image is acquired using Fatkun Batch Downloader to download large number of images. The project is develop using Tensorflow in Ubuntu operating system and interface is in HTML connected to the Python script via Flask. The results of the experiment show that CNN can identify with 92.7 percent of final accuracy with learning saturation (overfitting) of 500 cycle. While testing results shows 62.5 percent of accuracy in predicting new datasets.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Pneumonia Classification of Thorax Images using Convolutional Neural Networks

Jurnal INFORM ◽  
2020 ◽  
Vol 5 (2) ◽  
pp. 62-68
Author(s):  
Mahmud Suyuti ◽  
Endang Setyati
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Technology Development ◽  
Image Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computing Technology ◽  
Correction Technique ◽  
High Level

The digital image processing technique is a product of computing technology development. Medical image data processing based on a computer is a product of computing technology development that can help a doctor to diagnose and observe a patient. This study aimed to perform classification on the image of the thorax by using Convolutional Neural Network (CNN).  The data used in this study is lung thorax images that have previously been diagnosed by a doctor with two classes, namely normal and pneumonia. The amount of data is 2.200, 1.760 for training, and 440 for testing. Three stages are used in image processing, namely scaling, gray scaling, and scratching. This study used Convolutional Neural Network (CNN) method with architecture ResNet-50. In the field of object recognition, CNN is the best method because it has the advantage of being able to find its features of the object image by conducting the convolution process during training. CNN has several models or architectures; one of them is ResNet-50 or Residual Network. The selection of ResNet-50 architecture in this study aimed to reduce the loss of gradients at certain network-level depths during training because the object is a chest image of X-Ray that has a high level of visual similarity between some pathology. Moreover, several visual factors also affect the image so that to produce good accuracy requires a certain level of depth on the CNN network. Optimization during training used Adaptive Momentum (Adam) because it had a bias correction technique that provided better approximations to improve accuracy. The results of this study indicated the thorax image classification with an accuracy of 97.73%.

scholarly journals Diagnosing Chronic Glaucoma Using Watershed and Convolutional Neural Network

2021 ◽  
Vol 9 (9) ◽  
pp. 272-283
Author(s):  
Naureen Fathima
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Three Dimensional ◽  
Image Plane ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Computational Technique ◽  
Fundus Imaging

Abstract: Glaucoma is a disease that relates to the vision of human eye,Glaucoma is a disease that affects the human eye's vision. This sickness is regarded as an irreversible condition that causes eyesight degeneration. One of the most common causes of lifelong blindness is glaucoma in persons over the age of 40. Because of its trade-off between portability, size, and cost, fundus imaging is the most often utilised screening tool for glaucoma detection. Fundus imaging is a two-dimensional (2D) depiction of the three-dimensional (3D), semitransparent retinal tissues projected on to the imaging plane using reflected light. The idea plane that depicts the physical display screen through which a user perceives a virtual 3D scene is referred to as the "image plane”. The bulk of current algorithms for autonomous glaucoma assessment using fundus images rely on handcrafted segmentation-based features, which are influenced by the segmentation method used and the retrieved features. Convolutional neural networks (CNNs) are known for, among other things, their ability to learn highly discriminative features from raw pixel intensities. This work describes a computational technique for detecting glaucoma automatically. The major goal is to use a "image processing technique" to diagnose glaucoma using a fundus image as input. It trains datasets using a convolutional neural network (CNN). The Watershed algorithm is used for segmentation and is the most widely used technique in image processing. The following image processing processes are performed: region of interest, morphological procedures, and segmentation. This technique can be used to determine whether or not a person has Glaucoma. Keywords: Recommender system, item-based collaborative filtering, Natural Language Processing, Deep learning.

scholarly journals Convolutional Neural Network for Track Seed Filtering at the CMS High-Level Trigger

2018 ◽  
Vol 1085 ◽  
pp. 042040
Author(s):  
Adriano Di Florio ◽  
Felice Pantaleo ◽  
Antonio Carta ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Level Trigger ◽  
High Level

scholarly journals Inferring Emotion Tags from Object Images Using Convolutional Neural Network

2020 ◽  
Vol 10 (15) ◽  
pp. 5333
Author(s):  
Anam Manzoor ◽  
Waqar Ahmad ◽  
Muhammad Ehatisham-ul-Haq ◽  
Abdul Hannan ◽  
Muhammad Asif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Real Life ◽  
Vital Role ◽  
Object Categories ◽  
Different Types ◽  
Gender Based ◽  
High Level ◽  
Human Behavior Analysis

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.

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