Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Andrew, Andrew; Santoso, Handri

doi:10.33395/sinkron.v7i2.11383

Cited by 8 publications

(7 citation statements)

References 25 publications

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“…ResNet50, short for residual networks, is a 50-layer model that uses the concept of skipping levels to reduce overfitting, solve the problem of vanishing gradient, and ensure that higher layers function as well as lower ones [22]. Figure 3 shows the detailed architecture of the ResNet50 [23]. MobileNetV2 incorporates a unique layer module, the inverted residual with linear bottleneck, greatly decreasing the amount of memory required for processing [26].…”

Section: Model Architecturementioning

confidence: 99%

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Aufar¹,

Abdillah²,

Romadoni³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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In the agriculture industry, plant diseases provide difficulty, particularly for Arabica coffee production. A first step in eliminating and treating infections to avoid crop damage is recognizing ailments on Arabica coffee leaves. Convolutional neural networks (CNN) are rapidly advancing, making it possible to diagnose Arabica coffee leaf damage without a specialist's help. CNN is aimed to find features adaptively through backpropagation by adding layers including convolutional layers and pooling layers. This study aims to optimize and increase the accuracy of Arabica coffee leaf disease classification utilizing the neural network architectures: ResNet50, InceptionResNetV4, MobileNetV2, and DensNet169. Additionally, this research presents an interactive web platform integrated with the Arabica coffee leaf disease prediction system. Inside this research, 5000 image data points will be divided into five classes—Phoma, Rust, Cescospora, healthy, and Miner—to assess the efficacy of CNN architecture in classifying images of Arabica coffee leaf disease. 80:10:10 is the ratio between training data, validation, and testing. In the testing findings, the InceptionResnetV2 and DensNet169 designs had the highest accuracy, at 100%, followed by the MobileNetV2 architecture at 99% and the ResNet50 architecture at 59%. Even though MobileNetV2 is not more accurate than InceptionResnetV2 and DensNet169, MobileNetV2 is the smallest of the three models. The MobileNetV2 paradigm was chosen for web application development. The system accurately identified and advised treatment for Arabica coffee leaf diseases, as shown by the system's implementation outcomes.

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Section: Model Architecturementioning

confidence: 99%

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Aufar¹,

Abdillah²,

Romadoni³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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“…Due to its success, CNN has been implemented for some purposes. Andrew and Santoso [13] used three models, namely VGG19, ResNet50, and Inception-V3, to analyze the rating of online food orders. Their research found that the accuracy of VGG19 is 96.86, Resnet50 is 97.29, and Inception_v3 is 97.57.…”

Section: Literatures Reviewmentioning

confidence: 99%

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

Suwarno

2023

SinkrOn

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Fingerprint-based gender classification is beneficial for speeding up the fingerprint identification of criminals, accident victims, and natural disaster victims that are difficult to be recognized based on their physical characteristics. The biggest obstacle to digitally classifying fingerprints is the image's poor quality. Some methods have been developed to improve image quality through various preprocessing, such as noise removal, background segmentation, thinning, and binarization. However, as these processes increase the classification time, some methods have been developed to classify fingerprints without preprocessing. One of them that has shown excellent success is CNN (Convolutional Neural Network). The method does not require preprocessing, but the computation time is very long and requires large amounts of training data. This study proposed a new method that did not need any preprocessing by using wavelet decomposition combined with the max-pooling process to generate features. Firstly, the fingerprint image was decomposed with a Haar wavelet of 4 levels, and each level was followed by a max-pooling process with a 2´2 filter. After that, the resulting feature was used as training data for the Multilayer Perceptron (MLP) network. In this study, the training data was a dataset from NIST (National Institute of Standart and Technology), with 750 fingerprints consisting of male and female fingerprints, each as many as 375. The method could produce a total accuracy of 80.1%.

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“…It is based on the original paper "Rethinking the Inception Architecture for Computer Vision" [11]. The steps of the Inception process are convolution, pooling, dropout, fully connected, and softmax [12,13].…”

Section: Proposed Strategymentioning

confidence: 99%

The Use of Artificial Intelligence in the Classification of Medical Images of Brain Tumors

Almeida

2023

BJSTR

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Early detection in diagnosing brain diseases is crucial for proper disease treatment measures. The use of Artificial Intelligence/Machine Learning has contributed to the classification of diseases through medical images. Algorithms, models, and increasingly powerful techniques have emerged to assist physicians in this decision-making process. This research proposes to classify medical images using pretrained neural networks. The VGG16 model outperformed the other studied models, including VGG19, InceptionResNetV2, and InceptionV3. Models were evaluated by brain disease class, which included notumor, pituitary, glioma, and meningioma. The metrics used to evaluate the models included accuracy, precision, sensitivity, and specificity, all registering values above 95%. Furthermore, the Diagnostic Odds Ratio was above 473.00, indicating excellent responses based on the medical images. Overall, the results suggest that Artificial Intelligence techniques have made significant contributions to the early diagnosis of brain tumors.

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Compare VGG19, ResNet50, Inception-V3 for Review Food Rating

Cited by 8 publications

References 25 publications

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Web-based CNN Application for Arabica Coffee Leaf Disease Prediction in Smart Agriculture

Gender Classification Based on Fingerprint Using Wavelet and Multilayer Perceptron

The Use of Artificial Intelligence in the Classification of Medical Images of Brain Tumors

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