Layers Modification of Convolutional Neural Network for Pneumonia Detection

Setiawan, Wahyudi; Damayanti, Fitri

doi:10.1088/1742-6596/1477/5/052055

Cited by 19 publications

(10 citation statements)

References 2 publications

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“…Under the initial conditions used, VGG19 performs better than VGG16. However, the VGG19 (with 20,024,384 trainable parameters) has 16 convolution layers, while the VGG16 (with 14,714,684 trainable parameters) has only 13 59 . It should be emphasized that the smaller VGG16 network was previously selected as the template for the detailed study of the mVGG16 classifier for recognizing canine emotions.…”

Section: Test Resultsmentioning

confidence: 99%

Categorization of emotions in dog behavior based on the deep neural network

Kowalczuk

Czubenko

Żmuda‐Trzebiatowska

2022

Computational Intelligence

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The aim of this article is to present a neural system based on stock architecture for recognizing emotional behavior in dogs. Our considerations are inspired by the original work of Franzoni et al. on recognizing dog emotions. An appropriate set of photographic data has been compiled taking into account five classes of emotional behavior in dogs of one breed, including joy, anger, licking, yawning, and sleeping. Focusing on a particular breed makes it easier to experiment and recognize the emotional behavior of dogs. To broaden our conclusions, in our research study we compare our system with other systems of different architectures. In addition, we also use modern transfer learning with augmentation and data normalization techniques. The results show that VGG16 and VGG19 are the most suitable backbone networks. Therefore, a certain deep neural network, named mVGG16, based on the suboptimal VGG16 has been created, trained and fine-tuned with transfer (without augmentation and normalization). The developed system is then tested against an internal test dataset. In addition, to show the robustness of the system, a set of external data outside the breed is also taken into account. Being able to detect unsafe dog behavior and rely on a generalization for other breeds is worth popularizing. Equally important are the possible applications of the system 2116

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Section: Test Resultsmentioning

confidence: 99%

Categorization of emotions in dog behavior based on the deep neural network

Kowalczuk

Czubenko

Żmuda‐Trzebiatowska

2022

Computational Intelligence

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“…This class score map is spatially averaged to be a fixed-size vector. For (Setiawan & Damayanti, 2020) Hien, 2020), VGG16 is a CNN architecture that was used to win the ImageNet ILSVR competition 2014. It is as yet considered as one of the outstanding vision model architecture.…”

Section: Vgg Network (Vgg16 and Vgg19) Architecturementioning

confidence: 99%

Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Menaouer

Zoulikha

El-Houda

et al. 2022

Journal of Information Technology Research

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Pneumonia is a life-threatening infectious disease affecting one or both lungs in humans. There are mainly two types of pneumonia: bacterial and viral. Likewise, patients with coronavirus can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases. Chest X-rays are the common method used to diagnose coronavirus pneumonia and it needs a medical expert to evaluate the result of X-ray. Furthermore, DL has garnered great attention among researchers in recent years in a variety of application domains such as medical image processing, computer vision, bioinformatics, and many others. In this paper, we present a comparison of Deep Convolutional Neural Networks models for automatically binary classification query chest X-ray & CT images dataset with the goal of taking precision tools to health professionals based on fined recent versions of ResNet50, InceptionV3, and VGGNet. The experiments were conducted using a chest X-ray & CT open dataset of 5856 images and confusion matrices are used to evaluate model performances.

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“…CNN architectures such as VGG16 and VGG19 were introduced which have thirteen and sixteen convolution layers [33], ResNet50 and ResNet101 have 50 and 101 convolution layers [34], DenseNet 121, DenseNet 201, and DenseNet 169 have 121, 201, and 169 convolution layers [35], MobileNetV2 consists of 2 blocks where each block consists of 2 convolution layers [36], and MobileNetV3 in its original form consisting of 5 convolution layers has been developed to become simpler to become 2 convolutional layers [37]. The CNN architecture is divided into three layers based on their function, i.e., Convolutional Layer (CONV), Subsampling Layer (SUBS), and Fully Connected Layer (FC).…”

Section: System Design 41 Cnn Designmentioning

confidence: 99%

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

Nugroho

Nugraheni

Putranto

et al. 2022

EMITTER Int'l J. of Engin. Technol.

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When the blood flow to the arteries in brain is blocked, its known as Ischemic stroke or blockage stroke. Ischemic stroke can occur due to the formation of blood clots in other parts of the body. Plaque buildup in arteries, on the other hand, can cause blockages because if it ruptures, it can form blood clots. The b-1000 Diffusion Weighted (DW) Magnetic Resonance Imaging (MRI) image was used in a general examination to obtain an image of the part of the brain that had a stroke. In this study, classifications used several variations of layer convolution to obtain high accuracy and high computational consumption using b-1000 Diffusion Weighted (DW) MR in ischemic stroke types: acute, sub-acute and chronic. Ischemic stroke was classified using five variants of the Convolutional Neural Network (CNN) architectural design, i.e., CNN1–CNN5. The test results show that the CNN5 architectural design provides the best ischemic stroke classification compared to other architectural designs tested, with an accuracy of 99.861%, precision 99.862%, recall 99.861, and F1-score 99.861%.

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Layers Modification of Convolutional Neural Network for Pneumonia Detection

Cited by 19 publications

References 2 publications

Categorization of emotions in dog behavior based on the deep neural network

Categorization of emotions in dog behavior based on the deep neural network

Coronavirus Pneumonia Classification Using X-Ray and CT Scan Images With Deep Convolutional Neural Network Models

Classification of Ischemic Stroke with Convolutional Neural Network (CNN) approach on b-1000 Diffusion-Weighted (DW) MRI

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