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Author: search.filters.author.Hamamoto, KazuhikoSubject: search.filters.subject.Support vector machine

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    Hybrid learning of hand-crafted and deep-activated features using particle swarm optimization and optimized support vector machine for tuberculosis screening
    (2020-09-01)
    Win, Khin Yadanar
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    Maneerat, Noppadol
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    Hamamoto, Kazuhiko
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    Sreng, Syna
    Tuberculosis (TB) is a leading infectious killer, especially for people with Human Immunodeficiency Virus (HIV) and Acquired Immunodeficiency Syndrome (AIDS). Early diagnosis of TB is crucial for disease treatment and control. Radiology is a fundamental diagnostic tool used to screen or triage TB. Automated chest x-rays analysis can facilitate and expedite TB screening with fast and accurate reports of radiological findings and can rapidly screen large populations and alleviate a shortage of skilled experts in remote areas. We describe a hybrid feature-learning algorithm for automatic screening of TB in chest x-rays: it first segmented the lung regions using the DeepLabv3+ model. Then, six sets of hand-crafted features from statistical textures, local binary pattern, GIST, histogram of oriented gradients (HOG), pyramid histogram of oriented gradients and bags of visual words (BoVW), and nine sets of deep-activated features from AlexNet, GoogLeNet, InceptionV3, XceptionNet, ResNet-50, SqueezeNet, ShuffleNet, MobileNet, and DenseNet, were extracted. The dominant features of each feature set were selected using particle swarm optimization, and then separately input to an optimized support vector machine classifier to label 'normal' and 'TB' x-rays. GIST, HOG, BoVW from hand-crafted features, and MobileNet and DenseNet from deep-activated features performed better than the others. Finally, we combined these five best-performing feature sets to build a hybrid-learning algorithm. Using the Montgomery County (MC) and Shenzen datasets, we found that the hybrid features of GIST, HOG, BoVW, MobileNet and DenseNet, performed best, achieving an accuracy of 92.5% for the MC dataset and 95.5% for the Shenzen dataset.
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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%.
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    Classification of Cotton Wool Spots Using Principal Components Analysis and Support Vector Machine
    (2019-01-10)
    Sreng, Syna
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    Maneerat, Noppadol
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    Win, Khin Yadanar
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    Hamamoto, Kazuhiko
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    Panjaphongse, Ronakorn
    Diabetic retinopathy is a complication of the eye damage and can lead to being blindness if it is late for treatment. Microaneurysms, exudates, hemorrhages and cotton wool spots are the lesions associated with diabetic retinopathy. Numerous studies have been done on the detection of microaneurysms, and hemorrhages, as well as exudates whereas only a few research works for detection of cotton wool spots, mainly because of the fact that its appearances are difficult to filter out from the background and not clearly visible. In this paper, an algorithm is proposed to detect cotton wool spots based on integrating principal components analysis and support vector machine. First, preprocessing is performed to enhance the retinal images. Then adaptive thresholding method is used to roughly extract the cotton wool spot from the background. Support vector machine and principal components analysis are further applied respectively to select the important features from morphologies, first-order statistics, gray level occurrence matrix and lacunarity. The proposed method was evaluated with local and DIARETDB1 datasets containing 289 images. Given a success rate of accuracy 90.47 %, sensitivity 85.29%, and specificity 90.12% with the average computational time 16.47 seconds per image on cotton wool spots detection, this system performed better by comparing to the previous research works.
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    Medical image compression using vector quantization and system error compression
    (2015-09-01)
    Phanprasit, Tanasak
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    Hamamoto, Kazuhiko
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    Sangworasil, Manas
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    Pintavirooj, Chuchart
    A novel medical image compression scheme based on vector quantization (VQ) is proposed in this paper. The advantages of the technique are not only that it yields high compression ratio but also that it maintains a peak signal-to-noise ratio (PSNR). This new method involves three steps. First, we present a codebook design using discrete wavelet transform (DWT), fuzzy C-means (FCM), and support vector machine (SVM) algorithms. Second, we improve the bit rate using the Huffman coding theme as a method of eliminating the redundant index. Finally, we supplement the system with error compensation to improve the PSNR. With the proposed method, we are able to achieve a bit rate improvement of 24.00% and a PSNR of 10.96% over the conventional method.