Deep learning based image classification for intestinal hemorrhage

Pannu, Husanbir Singh; Ahuja, Sahil; Dang, Nitin; Soni, Sahil; Malhi, Avleen

doi:10.1007/s11042-020-08905-7

Cited by 39 publications

(7 citation statements)

References 43 publications

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“…A standard CNN architecture has more than one convolutional layer that extracts the rich and discriminative features from the input, a pooling layer to decrease the parameters and computations in the network, and a fully connected layer refers to the neural network. A CNN model is built by combining one or more above layers and perform a specific task by adjusting its internal parameters such as object detection, segmentation, and classification [ 6 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

Dash

Mohapatra

2021

Multimed Tools Appl

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The outbreak of coronavirus disease 2019 (COVID-19) continues to have a catastrophic impact on the living standard of people worldwide. To fight against COVID-19, many countries are using a combination of containment and mitigation activities. Effective screening of contaminated patients is a critical step in the battle against COVID-19. During the early medical examination, it was observed that patient having abnormalities in chest radiography images shows the symptoms of COVID-19 infection. Motivated by this, in this article, we proposed a unique framework to diagnose the COVID-19 infection. Here, we removed the fully connected layers of an already proven model VGG-16 and placed a new simplified fully connected layer set that is initialized with some random weights on top of this deep convolutional neural network, which has already learned discriminative features, namely, edges, colors, geometric changes,shapes, and objects. To avoid the risk of destroying the rich features, we warm up our FC head by seizing all layers in the body of our network and then unfreeze all the layers in the network body to be fine-tuned.The suggested classification model achieved an accuracy of 97.12% with 99.2% sensitivity and 99.6% specificity for COVID-19 identification. This classification model is superior to the other classification model used to classify COVID-19 infected patients.

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Section: Methodsmentioning

confidence: 99%

A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

Dash

Mohapatra

2021

Multimed Tools Appl

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“…Some of them are relied on image processing algorithms, as in [5], which used many image processing techniques for the Indian paper currency recognition model with an accuracy of 90 %. Meanwhile, other authors have relied on deep learning and machine learning algorithms using feature extraction for an image, as in [6], which used DL in image classification to classify the intestinal hemorrhage with an accuracy of 95 %. For instance, in [7], a system that can deal with a bionic eyeglass, which combines the functions of visual detection and recognition, was introduced.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…Therefore, the problem deals with image classification, and it would be the most suitable approach in solving such a problem. Deep learning is the relevant solution, as it matches our case that is needing to classify an image of a certain set of paper currency images and to provide a new prediction based on an extracted model from the deep learning method [5,6].…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based iraqi banknotes classification system for blind people

Awad¹,

Sharef

Salih

et al. 2022

EEJET

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Modern systems have been focusing on improving the quality of life for people. Hence, new technologies and systems are currently utilized extensively in different sectors of our societies, such as education and medicine. One of the medical applications is using computer vision technology to help blind people in their daily endeavors and reduce their frequent dependence on their close people and also create a state of independence for visually impaired people in conducting daily financial operations. Motivated by this fact, the work concentrates on assisting the visually impaired to distinguish among Iraqi banknotes. In essence, we employ computer vision in conjunction with Deep Learning algorithms to build a multiclass classification model for classifying the banknotes. This system will produce specific vocal commands that are equivalent to the categorized banknote image, and then inform the visually impaired people of the denomination of each banknote. To classify the Iraqi banknotes, it is important to know that they have two sides: the Arabic side and the English side, which is considered one of the important issues for human-computer interaction (HCI) in constructing the classification model. In this paper, we use a database, which comprises 3,961 image samples of the seven Iraqi paper currency categories. Furthermore, a nineteen layers Convolutional Neural Network (CNN) is trained using this database in order to distinguish among the denominations of the banknotes. Finally, the developed system has exhibited an accuracy of 98.6 %, which proves the feasibility of the proposed model.

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“…Under the normal use of data semantic information obtained by deep convolution, the local detail features reinforce information obtained by shallow convolution. e classification goal of higher accuracy can be achieved [14][15][16][17].…”

Section: Flow Rate Classification Network Based On Multifeature Fusionmentioning

confidence: 99%

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

Wang

Chen

Wang

2022

Computational Intelligence and Neuroscience

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The necessity of predicting and estimating river velocity motivates the development of a prediction method based on GAN image enhancement and multifeature fusion. In this method, in order to improve the image quality of river velocity, GAN network is used to enhance the image, so as to improve the integrity of image data set. In order to improve the accuracy of prediction, the image is extracted and fused with multiple features, and the extracted multiple features are taken as the input of CNN, so as to improve the prediction accuracy of convolution neural network. The results show that when the velocity is 0.25 m/s, 0.50 m/s, and 0.75 m/s, the accuracy of improved method can reach 85%, 90%, and 92%, which are higher than SVM, VGG-16, and BPNET algorithms. The above results indicate that the improvement has certain positive value and practical application value.

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Deep learning based image classification for intestinal hemorrhage

Cited by 39 publications

References 43 publications

A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

A Fine-tuned deep convolutional neural network for chest radiography image classification on COVID-19 cases

Deep learning-based iraqi banknotes classification system for blind people

Prediction and Estimation of River Velocity Based on GAN and Multifeature Fusion

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