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Computer-assisted screening for cervical cancer using digital image processing of pap smear images
(2020-03-01)
Win, Kyi Pyar
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Kitjaidure, Yuttana
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Hamamoto, Kazuhiko
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Aung, Thet Myo
Cervical cancer can be prevented by having regular screenings to find any precancers and treat them. The Pap test looks for any abnormal or precancerous changes in the cells on the cervix. However, the manual screening of Pap smear in the microscope is subjective with poorly reproducible criteria. Therefore, the aim of this study was to develop a computer-assisted screening system for cervical cancer using digital image processing of Pap smear images. The analysis of Pap smear image is important in the cervical cancer screening system. There were four basic steps in our cervical cancer screening system. In cell segmentation, nuclei were detected using a shape-based iterative method, and the overlapping cytoplasm was separated using a marker-control watershed approach. In the features extraction step, three important features were extracted from the regions of segmented nuclei and cytoplasm. RF (random forest) algorithm was used as a feature selection method. In the classification stage, bagging ensemble classifier, which combined the results of five classifiers-LD (linear discriminant), SVM (support vector machine), KNN (k-nearest neighbor), boosted trees, and bagged trees-was applied. SIPaKMeD and Herlev datasets were used to prove the effectiveness of our proposed system. According to the experimental results, 98.27% accuracy in two-class classification and 94.09% accuracy in five-class classification was achieved using the SIPaKMeD dataset. When the results were compared with five classifiers, our proposed method was significantly better in two-class and five-class problems.
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Cervical cancer detection and classification from pap smear images
(2019-09-16)
Win, Kyi Pyar
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Kitjaidure, Yuttana
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Paing, May Phyu
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Hamamoto, Kazuhiko
In this paper, we propose a framework for detection and classification of cervical cancer from pap smear images. Early detection and accurate diagnosis of cervical cancer can reduce the death rate of cervical cancer patients. Pap smear or pap test is the most popular technique for early detection of cervical cancer. However, the manual analysis is labor intensive and time consuming process which relies on expert cytologist. Hence, it is needed to develop a computer aided diagnosis system to make the pap smear test more accurate and reliable. The objective of this paper is to present an innovative idea of applying random forest algorithm (RF) as a feature selection method using proposed bagging ensemble classifier for improving the predictive performance. The four basic steps of cervical cancer detection and classification system, image enhancement, segmentation, feature extraction and classification were used. K-means clustering combining with morphology operations obtained good segmentation for cell nuclei and cytoplasm. The most important features, shape, color and texture of nuclei and cytoplasm were applied to detect cervical cancer. To improve the accuracy of prediction results, random forest (RF) algorithm was used as a feature selection method. In classification stage, bagging ensemble classifier was applied which aggregated the results of five classifiers, linear discriminant (LD), support vector machine (SVM), weighted k-nearest neighbor (KNN), boosted trees and bagged trees. Herlev data set was used to prove the effectiveness of our proposed method. According to the experimental results, the high classification accuracy was achieved with top10 features using our proposed combined classifier. The accuracy was 97.83% in two class problem and 81.54% in seven class problem. When the results were compared with five classifiers, our proposed method was significantly better in two class and seven class problems.
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Music genre classification of audio signals using particle swarm optimization and stacking ensemble
(2019-03-01)
Leartpantulak, Krittika
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Kitjaidure, Yuttana
Genre classification is a process of grouping similarities, such as patterns, styles, or objectives with management data as already in the music (e.g. pop and rock). It is used along with the classification of topics. This paper will classify songs from audio signal to a hierarchy of musical genre by using feature extraction. Trimbral texture, rhythmic content and pitch content are used as the main feature sets. Feature selection is selected by using Particle Swarm Optimization (PSO) and sent selected feature to classification. The result in classification has low accuracy. Thus, using stacking ensemble method is to improve the prediction. In this paper, the purpose is to improve the prediction by using stacking ensemble method. Stacking ensemble that have the second level is base classifier and meta-classifier. In base classifier consists of 5 classification; K-Nearest Neighbors (k-NN), Decision Tree (DT), Random Forest, Support Vector Machines (SVM); and Naïve Bayes. This process is generated to build multiple classifier predictors and sent it to meta-classifier. In the process of meta-classifier will create new model to predict test data. The new model has been created from neural network which train data is the output of base classifier.
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The Walking Assistance System using the Lower Limb Exoskeleton Suit Commanded by Backpropagation Neural Network
(2019-01-10)
Karantarat, Obnithi
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Kitjaidure, Yuttana
Currently there are many elderly people who have walking problems. This paper aims to develop and solve these problems by introducing walking assistance system which can recognize 3 types of gestures, include walking, sitting and standing. Our system is divided into 3 main parts including Feature extraction which consists of Time domain and Frequency domain, Classification and Exoskeleton suit system. Conjugate Gradient Backpropagation Neural Network is used to classify sEMG signal of lower limb posture after extracted the features. Then the output of classification is used to command the Exoskeleton suit to perform the gesture according to the results of the recognition. In addition, our paper uses PID controller to control DC motor of Four Bar Linkages Mechanisms of Lower Limb Exoskeleton suit in order to reduce the number of motors and increase stability during the Stance Phase. The results from the experiment have concluded that all feature in time domain has the most recognition rate which up to 99.39%.
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A Novel Feature Extraction for American Sign Language Recognition Using Webcam
(2019-01-10)
Thongtawee, Ariya
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Pinsanoh, Onamon
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Kitjaidure, Yuttana
Sign language is physical communication for contributing the meaning instead of using voice to demonstrate communicator's opinion. This paper introduces a simple and efficient algorithm for feature extraction to recognize American Sign Language alphabets from both static and dynamic gestures. The proposed algorithm comprises of four different techniques: Number of white pixels at the edge of the image (NwE), Finger length from the centroid point (Fcen), Angles between fingers (AngF) and Differences of angles between fingers of the first and last frame (delAng). After extracting features from video images, an Artificial Neural Network (ANN) is used to classify the signs. The result of these experiments is achieved up to 95% recognition rate, which is clearly to be the highest accuracy comparing with the other research worked in this field.
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Automatic stitching of medical images using feature based approach
(2019-01-01)
Win, Kyi Pyar
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Kitjaidure, Yuttana
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Hamamoto, Kazuhiko
Image stitching is a process of creating a panoramic image by combining multiple images that have overlapping regions of the same scene. It is a challenging topic in image processing, multimedia, and medical applications. The proposed system can be applied in medical applications for scoliosis operations and other long limb operations. The problem of the traditional x-ray machine is the narrow view. So, it can’t produce the large view of body x-ray image in a single frame. Therefore, this problem is solved by combining two or more x-ray images into a panorama one. This paper proposes a system which automatically stitches the x-ray images and produced a panorama x-ray image. The proposed stitching method is based on feature based approach, ORB (Oriented FAST and Rotated BRIEF). In feature detection stage, Oriented FAST approach is used. In feature description stage, Rotated BRIEF approach is applied. The two important criterias for determining the stitching performance are stitched image quality and processing time. Therefore, the aim of my proposed system is to produce a panorama x-ray image with high resolution and low processing time based on feature extraction approach. We compared our proposed method with three different features detectors. SIFT, SURF, and Harris corner detectors were tested and measured the rate of correct features detection and computation time. Finally, we measured the quality of result images that produced by stitching system of different feature based methods. According to the experimental results, ORB approach can produce high quality panorama image with least processing time.
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Hybrid dimensionality reduction of multi-sets using nature inspired algorithms and Discriminant Canonical Correlation Analysis for automatic sleep stage classification
(2019-01-01)
Moeynoi, Pimporn
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Kitjaidure, Yuttana
Automatic sleep stage classification using various signals is an important tool to assess sleep disorders and sleep quality. However, the numerous variables from multi sources lead to the classification problems. To improve the problem, this paper proposes a new hybrid dimensionality reduction by combining the feature selection based on the nature-inspired algorithms (NIAs) and multi-sets transformation technique by Canonical Correlation Analysis (CCA)/ Discriminant Canonical Correlation analysis (DCCA). The NIAs is first adopted to generate the updated population positions in each dataset, and then the CCA/DCCA is used to fuse the selected subsets. The proposed algorithm performance is demonstrated on sleep-wake detection and multi-class sleep stage classification. Furthermore, the proposed method called Dissimilarity Binary Grey Wolf Optimization - Discriminant Canonical Correlation Analysis (DisBGWO-DCCA), modified by using the differential evolution (DE) technique based on the similarity of Jaccard coefficient, provides the best classification accuracies with 97.75% of the sleep-wake detection and 95.85% of the multi-class sleep stage classification when comparing with other single dimensionality reduction approaches[1-2]. Moreover, the proposed algorithm achieves the computational cost better than the conventional NIAs as shown later in experiment. Our experiment is also operated on both healthy subjects and sleep disorder patients with efficient sleep stage classification.
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Biomedical Images Stitching using ORB Feature Based Approach
(2018-11-27)
Win, Kyi Pyar
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Kitjaidure, Yuttana
This paper proposes a system for biomedical images stitching using feature based approach. The proposed system aims to stitch the high resolution images with low processing time. The proposed system is designed with five stages., preprocessing., features extraction., features matching., homography estimation and images stitching. In feature detection stage., ORB feature based approach is used. The proposed method is improved in term of performance and accuracy. The proposed method was compared with many different features detectors., Harris corner detector., SIFT and SURF techniques. According to the experiments., ORB method had the better results than the other feature based methods in the detection rate of the corrected keypoints and processing time.
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A neural network PID-like controller using a hybrid of online Actor-Critic reinforcement algorithm with the square root cubature Kalman filter
(2018-01-01)
Sento, Adna
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Kitjaidure, Yuttana
This paper presents a new model of the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm, called the Neural Network PID-Like controller using an Actor-Critic reinforcement algorithm (NNPID-AC). The proposed NNPID-AC controller is designed to develop the performances and the speed of calculation under the iterative learning algorithm. In the learning algorithm, the critic algorithm receives the reward value and control input to criticize the current state using the action-state value function approximation. Furthermore, instead of applying every available action to predict the local successor state, the algorithm only uses one-step estimation using the fifth degree spherical-radial cubature rule algorithm. To evaluate the proposed NNPID-AC controller, the robot arm MATLAB simulations have been implemented and provide the control system with the load and noise to prove the robustness and fault tolerance, respectively. From the results, the robot arm control system simulation under the control of the proposed NNPID-AC controller can potentially track the error and gives the best responses compared with the other conventional controller either with or without the load and the noise disturbance.
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Forward kinematic-like neural network for solving the 3D reaching inverse kinematics problems
(2017-11-03)
Srisuk, Pannawit
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Sento, Adna
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Kitjaidure, Yuttana
This paper presents the inverse kinematic solutions based on neural networks. General neural network approaches use data of the end-effector positions as an input and angle joints as an output to train the neural network for mapping the input to the output. However, the proposed method creates the custom networks from forward kinematic equations. This special structure makes the network like a position finder with ability to automatically adjust angle joints until the end-effector reaches the desired position by backpropagation with variable learning rate algorithm. Then, the solutions of angles can be found from the final weights and bias values. Moreover, the proposed network use less number of neurons and amount of the solution space is not depend on the training data. Finally, to evaluate the performance algorithm, the MATLAB Program is used to demonstrate a 4-DOF robotic arm movement in 3-dimensional. As a result, the proposed algorithm can help a robotic arm move to the desired position (3D reaching) quickly and correctly.

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