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Author: search.filters.author.Phankokkruad, ManopSubject: search.filters.subject.CNN

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    Improving the Sound Classification Accuracy Using CNN-LSTM and MFCC with Audio Augmentation for Diagnosing Respiratory Disease
    (2025-01-01)
    Phankokkruad, Manop
    ;
    Wacharawichanant, Sirirat
    Audio is vital information data for understanding various situations. A multitude of sound features can be explained by analysis through the audio signals. Numerous classification methods have been developed to study audio classification. This work studies the improvement of audio classification for the diagnosis of respiratory disease through the integration of audio data augmentation and CNN in conjunction with LSTM (CNN-LSTM). Furthermore, this paper focuses on audio data augmentation and feature extraction in the deep learning approach. This study proposed the CNN-LSTM model to diagnose respiratory disease by learning from the different audio datasets. The results reveal that the CNN-LSTM model attained an accuracy of 81.48%, precision of 0.8340, sensitivity of 0.6948, and F1-score of 0.7225. Considering the achieved F1-score, the CNN-LSTM model demonstrates a high level of diagnotic accuracy. Therefore, all evaluation evaluation parameters collectively indicate the robust performance of the proposed disease classification model.
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    Evaluation of Deep Transfer Learning Models in Glaucoma Detection for Clinical Application
    (2021-01-01)
    Phankokkruad, Manop
    The clinical information supports the doctors in diagnosing the diseases and making the right decisions. Glaucoma is the leading cause of irreversible blindness disease. Vision loss can be avoided by early stage detection and right treatment. This study has proposed the deep transfer learning of the CNN model for detecting the glaucoma using ResNet50V2, VGG16, InceptionV3, and Xception. The proposed models help in the diagnosis of the patients who have glaucoma. The model with CNN architecture was used to learn from training the Glaucoma image dataset. Since the existing dataset has a small number of images, this study uses the data augmentation techniques to increase the virtual number of images. The results reveal that the proposed models have performed the classification task for detecting glaucoma. The proposed model achieved an accuracy level of VGG16, RestNet50V2, InceptionV3, and Xception are 97.27%, 94.53%, 95.31%, and 94.92%, respectively. Furthermore, this study evaluates the models by considering the clinical performance parameters include accuracy, precision, specificity, sensitivity, and F1 score. All models provide the high confidence values. The evaluation reveals that the deep transfer learning model with VGG16 architecture is the highest performance in tests. The VGG16 model achieved the average AUC-ROC value of 98.94%.
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    A comparison of efficiency improvement for long short-term memory model using convolutional operations and convolutional neural network
    (2019-07-01)
    Phankokkruad, Manop
    ;
    Wacharawichanant, Sirirat
    This work studied the comparison of LSTM, ConvLSTM and CNN-LSTM model, that was applied for time series forecasting. We created the LSTM, CNN-LSTM, ConvLSTM model and configured the optimal parameters by using hyperparameters optimization techniques. All models were applied to two different datasets for forecasting the number of patients in the future. This work also applied the SeLu and ReLu activation function to avoid the problem of gradient vanishing and improve the self-normalizing. The results indicated that two models had skillful, and made the reliable forecasting in two datasets. This work benchmarked the model performance by calculating MAE, RMSE, and sMAPE, which was acceptable in all case study. The CNN-LSTM model with SeLu activation function gave highest forecasting efficiency for the data contain seasonal variation. LSTM model with SeLu activation function gave highest forecasting efficiency in the case of non-stationary data.
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    An application of convolutional neural network-long short-term memory model for service demand forecasting
    (2019-07-01)
    Phankokkruad, Manop
    ;
    Wacharawichanant, Sirirat
    The medical services are very important requirement for being healthy human. In order to ensure the availability of resources for the medicine needed, the most hospital makes an service demand estimation by forecasting a number of patients to provide the sufficient medical services. Therefore, the accurately forecast a number of patients would be valuable knowledge for managing. This work proposed the CNN-LSTM model, which was a combination of CNN and LSTM, to forecast the number of patients who used hospital services. The CNN model was used to interpret, and extract the features from the input data. Then, it was provided this information to the LSTM model for interpreting and making a forecast. The CNN-LSTM models were applied to forecast on the two datasets. The results indicated that CNN-LSTM model made reliable forecasting. This work measured the model performnace by calculating RMSE and MAE value. The result showed RMSE and MAE of the models were very low in all experiments. Forecasting the number of patients can help the hospital to estimate the service demand, make a better policy for managing the medical resources on demand, and improve the efficiency of medical services for the future.