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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection
(2025-07-01)
Maneerat, Noppadol
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Narkthewan, Athasart
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Hamamoto, Kazuhiko
Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. Early diagnosis and prompt treatment can cut off the rising number of TB deaths, and analysis of chest X-rays is a cost-effective method. We describe a deep learning-based cascade algorithm for detecting TB in chest X-rays. Firstly, the lung regions were segregated from other anatomical structures by an encoder–decoder with an atrous separable convolution network—DeepLabv3+ with an XceptionNet backbone, DLabv3+X, and then cropped by a bounding box. Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. Different combinations of trained DCNNs were compared, and the combination with the maximum accuracy was retained as the winning combination. The ensemble classifier was designed to predict the presence of TB by fusing DCNNs from the winning combination via weighted averaging. Our lung segmentation was evaluated on three publicly available datasets: it provided better Intercept over Union (IoU) values: 95.1% for Montgomery County (MC), 92.8% for Shenzhen (SZ), and 96.1% for JSRT datasets. For TB prediction, our ensemble classifier produced a better accuracy of 92.7% for the MC dataset and obtained a comparable accuracy of 95.5% for the SZ dataset. Finally, occlusion sensitivity and gradient-weighted class activation maps (Grad-CAM) were generated to indicate the most influential regions for the prediction of TB and to localize TB manifestations.
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Glioma Brain Tumor Classification Using Convolution Neural Network and Majority Voting
(2025-01-01)
Pilaoon, Pongsak
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Hamamoto, Kazuhiko
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Maneerat, Noppadol
Glioma brain tumors are malignant diseases for which early detection and instant treatment will increase the survival rate. Several studies have reported the efficiency of deep learning convolutional neural networks (CNN) in diagnosing brain tumors using magnetic resonance imaging (MRI). In this study, we investigated the potential of state-of-the-art classifiers to achieve the highest accuracy in the detection of brain tumors using MRI. For this purpose, we introduced a comparative study of eight state-of-the-art classifiers. The methodology comprised three different approaches: 1) an imbalanced dataset, 2) a balanced dataset using image augmentation, and 3) ensemble learning using the best of the top five models for majority hard and soft voting. The dataset comprised converted MRI data from the repository of molecular brain neoplasia data (REMBRANDT) and brain tumor segmentation 2021 (BraTS) databases. An increasing number of MRI images and datasets has prevented overfitting. Initially, a preprocessing stage morphological operation and contrast-limited adaptive histogram equalization (CLAHE) algorithms were used to remove skeletons and artifacts and optimize the image contrast for readiness classification. The stochastic gradient descent with the momentum algorithm option was used to train the network. The trained model was used to predict the testing dataset, and the results from each pretrained network were evaluated. The experimental results demonstrated that the prediction accuracy of the trained network was significantly improved using a balanced training dataset. The discriminative image region used to interpret the predicted result using the gradient-weighted class activation mapping (Grad-CAM) algorithm was proposed in the final stage for trustworthiness. The experimental results showed that the best approach was inceptionV3 with a balanced dataset. The accuracy, sensitivity, specificity, and area under the curve were 99.73%, 99.61%, 100%, and 1.00, respectively.
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Glioma Brain Tumor Classification using Transfer Learning
(2024-01-01)
Pilaoon, Pongsak
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Narkthewan, Athasart
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Wadlom, Noppanat
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Varakulsiripunth, Ruttikorn
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Hamamoto, Kazuhiko
In this research the glioma brain tumor binary classification using transfer learning was introduced. The MRI image from 2 datasets comprised with REMBRANDT and BraTS2021 with increasing number of MRI images was proposed to prevent overfitting problem. MRI images were converted to JPEG format and heavily imbalanced with normal brain image is minority class. Morphological operation was used to remove skeletons and artifacts from brain region. We have introduced Contrast Limited Adaptive Histogram Equalization to preprocess and enhance contrast before classify using various CNNs. To handle imbalanced dataset problem, we proposed image augmentation to increase the number of images and obtain balanced dataset. The various CNNs transfer learning was implemented to classify glioma brain tumor. Finally, the best classifier is InceptionV3 with balanced dataset that obtained accuracy 99.19%, sensitivity 98.83%, and specificity 100% respectively, better than our past research work.
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Brain Tumor Classification using Pretrained Deep Convolutional Neural Network
(2023-01-01)
Pilaoon, Pongsak
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Maneerat, Noppadol
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Nakthewan, Athasart
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Varakulsiripunth, Ruttikorn
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Hamamoto, Kazuhiko
In this research deep learning convolution neural network (CNN) has been implemented for binary classification GBM brain tumor. The dataset from REMBRANDT database that comprise of 155 MRI images has been utilized in this research. The transfer learning by pretrained network namely GoogleNet and AlexNet have been conducted to classify the GBM brain tumor form normal brain. The advantage of classification by transfer learning is the manual segmentation and feature extraction were replaced by automatic procedure that reduce the error from human handcraft. The prediction result by using Googlenet pretrained network shown accuracy result 80.85% and Alexnet pretrained network obtained accuracy 93.62%. The GBM brain tumor classification by deep learning pretrained network obtained good result of accuracy and can be implemented in practical to help the medical staff for earlier diagnosis for further treatment and increasing survivor rate of patients. The manual adjustment for segmentation and feature extraction are improved by automatic classification using deep learning pretrained network is main advantage of this research. The future work we will try to implement with increasing images dataset to improve accuracy, robustness testing and prevent overfitting problem.
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Brain Tumor Classification using Supervised Support Vector Machine
(2023-01-01)
Pilaoon, Pongsak
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Maneerat, Noppadol
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Yajima, Kuniaki
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Varakulsiripunth, Ruttikorn
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Hamamoto, Kazuhiko
A Glioblastoma (GBM) is a malignant brain tumor earlier detection and diagnosis will increase survival opportunities. This research has developed for binary classification of GBM brain tumors by supervised machine learning from magnetic resonance imaging (MRI). DICOM medical images have been converted into JPEG files and morphological operation has been implemented to separate the brain region from the skull image for preparation and easier for tumor segmentation in preprocessing stage. The global thresholding segmentation has been proposed to segment the brain tumor from the artifact and then the features have been extracted by gray level coefficient matrix feature extraction (GLCM). In this research, a support vector machine has been conducted for binary classification and finally, GBM grade-4 brain tumor is distinguished from normal brain images. The dataset comprises 155 MRI images 80% has been assigned for training and another 20% will be the testing dataset. The experimental output prediction result is 96.875 % accuracy, 95 % sensitivity, and 100% specificity. The performance of classification has been improved and shown better results when compared with previous research work.
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Banana Plant Nutrient Deficiencies Identification using Deep Learning
(2023-01-01)
Han, Kadipa Aung Myo
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Maneerat, Noppadol
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Sepsirisuk, Kasemsuk
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Hamamoto, Kazuhiko
This paper presents nutrient deficiency multi-class classification in banana plant data sets using a deep convolutional neural network. In this paper, healthy and eight nutrient deficiency classes were studied. The performance was evaluated in different situations of two public data sets. The proposed method can provide sensitivity and specificity in Raw Images, Raw Images with combination, Augmented Images, and Augmented Images with the combination. Furthermore, nearly 88% of the F1-score was outperformed.
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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
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Hamamoto, Kazuhiko
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Bui, Linh Khanh
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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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Automated Caries Screening Using Ensemble Deep Learning on Panoramic Radiographs
(2022-10-01)
Bui, Toan Huy
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Hamamoto, Kazuhiko
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Paing, May Phu
Caries prevention is essential for oral hygiene. A fully automated procedure that reduces human labor and human error is needed. This paper presents a fully automated method that segments tooth regions of interest from a panoramic radiograph to diagnose caries. A patient’s panoramic oral radiograph, which can be taken at any dental facility, is first segmented into several segments of individual teeth. Then, informative features are extracted from the teeth using a pre-trained deep learning network such as VGG, Resnet, or Xception. Each extracted feature is learned by a classification model such as random forest, k-nearest neighbor, or support vector machine. The prediction of each classifier model is considered as an individual opinion that contributes to the final diagnosis, which is decided by a majority voting method. The proposed method achieved an accuracy of 93.58%, a sensitivity of 93.91%, and a specificity of 93.33%, making it promising for widespread implementation. The proposed method, which outperforms existing methods in terms of reliability, and can facilitate dental diagnosis and reduce the need for tedious procedures.
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Multimodal Biometrics Recognition Using a Deep Convolutional Neural Network with Transfer Learning in Surveillance Videos
(2022-07-01)
Aung, Hsu Mon Lei
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Pluempitiwiriyawej, Charnchai
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Hamamoto, Kazuhiko
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Wangsiripitak, Somkiat
Biometric recognition is a critical task in security control systems. Although the face has long been widely accepted as a practical biometric for human recognition, it can be easily stolen and imitated. Moreover, in video surveillance, it is a challenge to obtain reliable facial information from an image taken at a long distance with a low-resolution camera. Gait, on the other hand, has been recently used for human recognition because gait is not easy to replicate, and reliable information can be obtained from a low-resolution camera at a long distance. However, the gait biometric alone still has constraints due to its intrinsic factors. In this paper, we propose a multimodal biometrics system by combining information from both the face and gait. Our proposed system uses a deep convolutional neural network with transfer learning. Our proposed network model learns discriminative spatiotemporal features from gait and facial features from face images. The two extracted features are fused into a common feature space at the feature level. This study conducted experiments on the publicly available CASIA-B gait and Extended Yale-B databases and a dataset of walking videos of 25 users. The proposed model achieves a 97.3 percent classification accuracy with an F1 score of 0.97and an equal error rate (EER) of 0.004.
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Tooth Localization using YOLOv3 for Dental Diagnosis on Panoramic Radiographs
(2022-01-01)
Bui, Toan Huy
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Hamamoto, Kazuhiko
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Paing, May Phu
Oral health is one of most major concerns that affect the life quality of billions of people around the world. Diagnosis treatment usually takes time due to the lack of doctors compared to a huge number of patients. Many researchers proposed methods to make an early disease detection for patients to assist doctors using computer aid diagnosis (CAD). However, most previous methods are not end-to-end methods and still require human involvement. The biggest challenge is that most researchers do not provide a good tooth detection technique before diagnosis. Therefore, the main objective, that builds a system to assist doctors, remains unaccomplished or just fairly successful. This paper proposed a detection method to localize the tooth using the Yolov3 model as a base network in the dental panoramic radiograph. The method consists of two main parts: image preprocessing and tooth localization. Firstly, because deep learning requires a big dataset, the original image is applied augmentation technique to improve the size of the dataset as well as diversity. Then, each image is resized to fit the input layer of the network; however, to prevent the information loss and boost the performance, we keep the original ratio of the images and change the ratio of the input layer in the model that can fit the image ratio. Next, we feed images into Yolov3, which is specially modified to fit the problem, for training. We add more detection heads into the backbone and concatenate the previous head detection’s result with a proper layer to produce a more preeminent result. The final assessment shows an impressive result that the method reaches 95.58% and 94.90% for precision and recall, respectively. As a result, our proposed method is more reliable and practical in the tooth localization field, as well as helpful to reduce the doctor's effort.

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