A Single Filter CNN Performance for Basic Shape Classification

Murata, Kenya; Mito, Masataka; Eguchi, Daisuke; Mori, Yuichiro; Toyonaga, Masahiko

doi:10.1109/icawst.2018.8517219

Cited by 6 publications

(3 citation statements)

References 2 publications

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“…This layer uses two types of processes: max-pooling and average pooling, which means selecting the maximum and minimum values in each feature map [11]. The fully-connected layer is a layer that has a complete connection to all activations in the previous layer followed by nonlinear functions, such as ReLU [28].…”

Section: A Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Novitasari,

Fatmawati,

Hendradi

et al. 2023

JOIV : Int. J. Inform. Visualization

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Diabetic Retinopathy (DR) is a complication of diabetes mellitus, which attacks the eyes and often leads to blindness. The number of DR patients is significantly increasing because some people with diabetes are not aware that they have been affected by complications due to chronic diabetes. Some patients complain that the diagnostic process takes a long time and is expensive. So, it is necessary to do early detection automatically using Computer-Aided Diagnosis (CAD). The DR classification process based on these several classes has several steps: preprocessing and classification. Preprocessing consists of resizing and augmenting data, while in the classification process, CSVM method is used. The CSVM method is a combination of CNN and SVM methods so that the feature extraction and classification processes become a single unit. In the CSVM process, the first stage is extracting convolutional features using the existing architecture on CNN. CSVM could overcome the shortcomings of CNN in terms of training time. CSVM succeeded in accelerating the learning process and did not reduce the accuracy of CNN's results in 2 class, 3 class, and 5 class experiments. The best result achieved was at 2 class classification using CSVM with data augmentation which had an accuracy of 98.76% with a time of 8 seconds. On the contrary, CNN with data augmentation only obtained an accuracy of 86.15% with a time of 810 minutes 14 seconds. It can be concluded that CSVM was faster than CNN, and the accuracy obtained was also better to classify DR.

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Section: A Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Novitasari,

Fatmawati,

Hendradi

et al. 2023

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…The process is repeated several times to filter the feature maps obtained with the use of subsequent convolutional kernels. Characteristic parameters of the convolution layer are the number and size of filters in individual layers, the step by which the window corresponding to the filter is moved (Murata et al, 2018). The pooling layer is usually placed between two convolutional layers (Zhao and Wang, 2019).…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

2022

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Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.

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“…Deep learning is an advanced machine learning implementation method based on artificial neural networks, popularly adopted in the past few years. Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are commonly used for pattern detection [24]- [26], object detection [27]- [29], image classification [30]- [32], and other purposes. At the same time, RNN has shortcomings, especially the problem of long-term dependence on time series data which causes loss of gradient, leading to the formation of the Long Short-Term Memory (LSTM) algorithm, which is a development of RNN in overcoming these problems.…”

Section: Introductionmentioning

confidence: 99%

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

Putri,

Caraka,

Toharudin

et al. 2024

IEEE Access

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Particulate matter forecasting is fundamental for early warning and controlling air pollution, especially PM2.5. The increase in this level of concentration will lead to a negative impact on public health. This study develops a hybrid model of CNN-LSTM and CONV-LSTM by combining a convolutional neural network (CNN) with an LSTM network to forecast PM2.5 concentration for the next few hours in Kemayoran DKI Jakarta, which is known as a busy area. We discovered the advantages of CNN in effectively extracting features and LSTM in learning long-term historical data from PM2.5 concentration time series data. The predictive model of CNN-LSTM is carried out in a different architecture where the CNN process is carried out first to become the input of LSTM. For CONV-LSTM, it is carried out in one architecture where the multiplication in the LSTM architecture is coupled with the convolution process. This research will explain how the method of developing hybrid CNN-LSTM and CONV-LSTM in predicting PM2.5 concentrations. Based on metric evaluation, the two models are compared to find the best model. Both predictive models produce MAPE values that fall into the good enough category with values <20%. Results were obtained for CONV-LSTM with MAE worth 6.52, RMSE 8.55, and MAPE 16.39%. As a result, the CONV-LSTM model performs better than CNN-LSTM in nowcasting PM2.5.

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A Single Filter CNN Performance for Basic Shape Classification

Cited by 6 publications

References 2 publications

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level

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A Single Filter CNN Performance for Basic Shape Classification

Cited by 6 publications

References 2 publications

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Automated Staging of Diabetic Retinopathy Using Convolutional Support Vector Machine (CSVM) Based on Fundus Image Data

Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal

Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM2.5 Level

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Fine-Tuning of Predictive Models CNN-LSTM and CONV-LSTM for Nowcasting PM_2.5 Level