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Author: search.filters.author.Hamamoto, KazuhikoSubject: search.filters.subject.Deep learning

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    Multi-Disease Classification of COVID-19 in Chest Radiographs using Ensemble of Optimized Deep Learning Models
    (2023-01-01)
    Bui, Toan Huy
    ;
    Hamamoto, Kazuhiko
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    Bui, Linh Khanh
    ;
    Paing, May Phu
    In recent years, COVID-19 has become the top concern of almost everyone around the world. Identifying COVID-19 infections is extremely important for appropriate treatment methods. Furthermore, classifying Covid along with other lung diseases also plays an important role. This article proposes a method for diagnosing COVID-19 and other common lung diseases on chest X-ray images using CADs. CNN models are used to learn informative features from patient chest X-ray images. The performance of diagnosis is optimized using the area under the curve maximization and proximal epoch stochastic optimization. Later, optimized results are employed to compute the most probabilities prediction and produce the latest result. The proposed method was tested on a large public radiograph dataset and reached 98.07%, 96.92%, and 96.36% for accuracy, precision, and recall, respectively. The performance is promising and comparable to other previous research but in a more complex dataset. Overall, this proposed method is trustworthy for medical doctors on COVID-19 detection among lung disease problems.
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    Ensemble deep learning for the detection of COVID-19 in unbalanced chest X-ray dataset
    (2021-11-01)
    Win, Khin Yadanar
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    Maneerat, Noppadol
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    Sreng, Syna
    ;
    Hamamoto, Kazuhiko
    The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.
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    Deep fusion feature extraction for caries detection on dental panoramic radiographs
    (2021-03-01)
    Bui, Toan Huy
    ;
    Hamamoto, Kazuhiko
    ;
    Paing, May Phu
    Caries is the most well-known disease and relates to the oral health of billions of people around the world. Despite the importance and necessity of a well-designed detection method, studies in caries detection are still limited and show a restriction in performance. In this paper, we proposed a computer-aided diagnosis (CAD) method to detect caries among normal patients using dental radiographs. The proposed method mainly consists of two processes: feature extraction and classification. In the feature extraction phase, the chosen 2D tooth image was employed to extract deep activated features using a deep pre-trained model and geometric features using mathematic formulas. Both feature sets were then combined, called fusion feature, to complement each other defects. Then, the optimal fusion feature set was fed into well-known classification models such as support vector machine (SVM), k-nearest neighbor (KNN), decision tree (DT), Naïve Bayes (NB), and random forest (RF) to determine the best classification model that fit the fusion features set and perform the most preeminent result. The results show 91.70%, 90.43%, and 92.67% for accuracy, sensitivity, and specificity, respectively. The proposed method has outperformed the previous stateof- the-art and shows promising results when none of the measured factors is less than 90%; therefore, the method is promising for dentists and capable of wide-scale implementation caries detection in hospitals.
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    Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images
    (2020-07-01)
    Sreng, Syna
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    Maneerat, Noppadol
    ;
    Hamamoto, Kazuhiko
    ;
    Win, Khin Yadanar
    Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.