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![Illustration of Max Pooling and Average Pooling Figure 2 above shows an example of max pooling operation and average pooling with a 2x2 pixel filter size from 4x4 pixel input. At max pooling, each filter is taken the maximum value, then arranged into a new output with a size of 2x2 pixels. While the average pooling value taken is the average value of the filter size. Classification layer is a layer consisting of flattening, hidden layer and activation functions. Hidden layers in artificial neural networks is layers between input layer and output layer, where artificial neurons take a set of weight inputs and produce output through activation functions such as sigmoid[8], ReLU[9], or Softmax[10].](publication/333593451/figure/fig2/AS:765890261966848@1559613876098/Illustration-of-Max-Pooling-and-Average-Pooling-Figure-2-above-shows-an-example-of-max.png)
Illustration of Max Pooling and Average Pooling Figure 2 above shows an example of max pooling operation and average pooling with a 2x2 pixel filter size from 4x4 pixel input. At max pooling, each filter is taken the maximum value, then arranged into a new output with a size of 2x2 pixels. While the average pooling value taken is the average value of the filter size. Classification layer is a layer consisting of flattening, hidden layer and activation functions. Hidden layers in artificial neural networks is layers between input layer and output layer, where artificial neurons take a set of weight inputs and produce output through activation functions such as sigmoid[8], ReLU[9], or Softmax[10].
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Nails are one part of the fingers and toes, by observing the shape and the condition of the nails, health expert can find out information about a person’s health. However, this sometimes not realized and ignored by society, even though many diseases that can be seen through the condition of the nails and the shape of the nails are one of the system...
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... Max and average pooling[21] ...
span lang="EN-US">Breast cancer represents one of the most common reasons for death in the worldwide. It has a substantially higher death rate than other types of cancer. Early detection can enhance the chances of receiving proper treatment and survival. In order to address this problem, this work has provided a convolutional neural network (CNN) deep learning (DL) based model on the classification that may be used to differentiate breast cancer histopathology images as benign or malignant. Besides that, five different types of pre-trained CNN architectures have been used to investigate the performance of the model to solve this problem which are the residual neural network-50 (ResNet-50), visual geometry group-19 (VGG-19), Inception-V3, and AlexNet while the ResNet-50 is also functions as a feature extractor to retrieve information from images and passed them to machine learning algorithms, in this case, a random forest (RF) and k-nearest neighbors (KNN) are employed for classification. In this paper, experiments are done using the BreakHis public dataset. As a result, the ResNet-50 network has the highest test accuracy of 97% to classify breast cancer images.</span
... Many common transfer learning tendencies exist, including VGG, ResNet, Inception, and others. The pre-trained CNN version Inception V3 [13] [14]. ...
One of the most difficult challenges is recognizing human actions., especially in still images where there isn't much movement. Therefore, Using the transfer learning strategy, we suggested a technique for identifying human action., which consists of training some of the layers of deep learning techniques while freezing others. Also presented a way for data split, which is to choose some frames because we are working on a large dataset such as UCF-101, and this method is summarized by discovering the features for each frame, then clustering the elements, and then choosing a percentage of each cluster for training and test data. We used three techniques. They are VGG16, Inception V3, and xception. The proposed models have been implemented on UCF-101 Dataset. Depending on three data split methods with the dataset, the random split method, and the proposed split method, the Inception V3 achieved the highest accuracy. In contrast, the VGG16 achieved the least accuracy, and the accuracy of the xception was close to that of the Inception V3. By comparing the size of the dataset, the proposed methods achieved good results: the VGG16 in the proposed split attained an accuracy of 92.5%, the Inception V3 in the proposed split attained an accuracy of 98.12%, and the xception in the proposed split attained an accuracy of 95.16%. The VGG16 network is simple, so the VGG16 is less accurate. While the network in Inception V3, xception, is more extensive and complex, the learning space is more significant, although the network size is more prominent in Inception V3, xception. We only trained some blocks in the top layer.
... function. The sample pooling layers structure [15] is shown in Figure 3. The full connection (FC) layer operates at the input where each input is connected to all neurons. ...
... The FC layer often uses the last layer to optimize the classification results. The sample FC layer [15] is shown in Figure 4. The Relu activation function is the most common function used in hidden layers () [16]. ...
Monkeypox disease is caused by a virus that causes lesions on the skin and has been observed on the African continent in the past years. The fatal consequences caused by virus infections after the COVID pandemic have caused fear and panic among the public. As a result of COVID reaching the pandemic dimension, the development and implementation of rapid detection methods have become important. In this context, our study aims to detect monkeypox disease in case of a possible pandemic through skin lesions with deep-learning methods in a fast and safe way. Deep-learning methods were supported with transfer learning tools and hyperparameter optimization was provided. In the CNN structure, a hybrid function learning model was developed by customizing the transfer learning model together with hyperparameters. Implemented on the custom model MobileNetV3-s, EfficientNetV2, ResNET50, Vgg19, DenseNet121, and Xception models. In our study, AUC, accuracy, recall, loss, and F1-score metrics were used for evaluation and comparison. The optimized hybrid MobileNetV3-s model achieved the best score, with an average F1-score of 0.98, AUC of 0.99, accuracy of 0.96, and recall of 0.97. In this study, convolutional neural networks were used in conjunction with optimization of hyperparameters and a customized hybrid function transfer learning model to achieve striking results when a custom CNN model was developed. The custom CNN model design we have proposed is proof of how successfully and quickly the deep learning methods can achieve results in classification and discrimination.
... Many popular transfer learning models exist such as VGG, ResNet, Inception, and others. Inception-v3 is a pre-trained CNN model, trained on millions of images on the ImageNet dataset, which classifies the network into 1000 categories [18] [19]. ...
Corona virus's correct and accurate diagnosis is the most important reason for contributing to the treatment of this disease. Radiography is one of the simplest methods to detect virus infection. In this research, a method has been proposed that can diagnose disease based on radiography (X-ray chest) and deep learning techniques. We conducted a comparative study by using three diagnosis models; the first one was developed by using traditional CNN, while the two others are our proposed models (second and third models). The proposed models can diagnose the COVID-19 infection, normal cases, lung opacity, and Viral Pneumonia according to the four categories in the covid19 radiography dataset. The transfer learning technology had used to increase the robustness and reliability of our model, also, data augmentation was used for reducing the overfitting and to increase the accuracy of the model by scaling rotation, zooming, and translation. The third model showed higher training accuracy of 93.18% compared to the two other models that are dependent on using traditional convolution neural networks with an accuracy of 70.28% of the first model, while the accuracy of the second model that uses data augmentation with traditional convolution neural is 90.1%, while the testing accuracy models was 68.27% for the first model, 87.55% for the second model, and 86.03% for the third model.
... There are several approaches to pooling. The most commonly used approaches are average pooling and max-pooling [31,32]. ...
Finger vein verification has recently gained the attention of many researchers as one of the most interesting biometrics. This paper proposes a deep learning model called the Deep Fingers Vein Learning (DFVL). to improve recognition accuracy by training a Convolutional Neural Network. The final model consists of the following layers: three convolutional & ReLU, pooling, fully connected, soft-max and classification. All this after the hand image goes through the basic stages of determining the region of interest by operations within the preprocessing. The effect of changing parameter values was examined, analyzed, and discussed. The best accuracy results recorded by tuning the network parameter is 81.7%. This percentage was increased to 89% after using the five-finger fusion (Correct match of three or more fingers).
... Xception has 36 convolutions layers. It consists of three (Yani, 2019) flows. The first flow is the entry flow which has convolution, separable convolution, and pooling layers. ...
The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively.
... EfficientNet's popularity among deep learning researchers is due to its high classification and prediction performances. EfficientNet's performance is compared with other deep learning models in various studies, and, EfficientNet is found to perform better than others (Nayak et al., 2022) (Yani & others, 2019). In another study (Abdulhadi, Al-Dujaili, Humaidi, & Fadhel, 2021), it was examined four forms of nail illnesses, including healthy nails, nail hyperpigmentation, nail clubbing, and nail fungus. ...
This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.
... For example, the 'Convolutional Neural Network (CNN) ' (Alqudah et al., 2020; won the competition for image characterisation. The author proposed the work (Hussain et al., 2018) and examined such a CNN framework, for example, inception-v3, to set up whether it operates well as far as precision on a dataset of images using transfer learning (Ahuja et al., 2021;Raghu et al., 2019;Yani et al., 2019). The proposed work was assessed, and the outcomes are contrasted with other best schemes (Ahmad et al., 2019). ...
The proportion of COVID-19 patients is significantly expanding around the world. Treatment with serious consideration has become a significant problem. Identifying clinical indicators of succession towards severe conditions is desperately required to empower hazard stratification and optimise resource allocation in the pandemic of COVID-19. Consequently, the classification of severity level is significant for the patient’s triaging. It is required to categorise the severity level as mild, moderate, severe, and critical based on the patients’ symptoms. Various symptomatic parameters may encourage the evaluation of infection seriousness. Likewise, with the rapid spread and transmissibility of COVID-19 patients, it is crucial to utilise telemonitoring schemes for COVID-19 patients. Telemonitoring mediation encourages remote data and information exchange among medicinal services, suppliers, and patients, furthermore, risk mitigation and provision of appropriate medical facilities. This paper provides explorative data analysis of symptoms, comorbidities, and other parameters, comparing different machine learning algorithms for case severity detection. This paper also provides a system (based on the degree of truthfulness) for case severity detection that might be utilised to stratify risk levels for anticipated moderate and severe COVID-19 patients. Finally, we provide a telemonitoring model of COVID-19 patients to ensure the remote and continuous monitoring of case severity progression and appropriate risk mitigation strategies.
... F1 Score = 2*(Recall * Accuracy) / (Recall + Accuracy) (3) Fig. 8 illustrates the contrast between loss of training and corresponding validation to the dataset. One of the main reasons for obtaining this precision resides in Average Pooling [24]. ...
... Salah satunya adalah Inception-V3. Inception-V3 ini adalah arsitektur yang dikembangkan berdasarkan Convolutional Neural Network [12]. Dalam framework keras terdapat juga beberapa macam optimizer. ...
Deep learning semakin berkembang pesat dan banyak dimanfaatkan dalam berbagai bidang kehidupan. Salah satunya bisa dimanfaatkan untuk klasifikasi image medis penderita covid. Keras adalah salah satu framework deep learning yang paling banyak digunakan. Dalam Keras, terdapat beberapa macam algoritma optimizer. Salah satunya adalah optimizer Adam. Untuk menggunakan optimizer Adam ini, perlu menentukan angka learning rate. Penentuan angka learning rate sangat penting karena salah dalam menentukan angka learning rate akan berdampak pada hasil deep learning yang dilakukan. Batch size juga salah satu hyperparameter penting dalam deep learning. Penelitian ini bertujuan untuk mengetahui dan membandingkan beberapa learning rate dan batch size agar diketahui efek dan dampaknya pada hasil loss dan akurasi training dan validasi pada proses deep learning yang dilakukan. Ada 6 learning rate dan 3 batch size yang akan dibandingkan. Hasil yang optimal diantara 6 learning rate dalam penelitian ini adalah 0.0001 dan 0.00001. Sedangkan batch size yang paling bagus hasilnya dari tiga angka yang dibandingkan adalah batch size 5
