Rex Paolo C. Gamara
16 Publications
Scopus ID: 85159487429
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
In medical imaging, an X-ray image generated using a flat panel detector (digital) typically has poor image quality, affecting the capability of successful medical diagnosis based on the images. The image enhancement process intends to provide better interpretability of the information contained in the images. The main problems considered for medical images include poor quality and low contrast. Therefore, the general objectives of image enhancement include contrast improvement and noise reduction. This study proposes an upgraded X-ray image enhancement hybrid algorithm that utilizes and consists of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method combined with the Wiener filter. Based on the performance metrics results, including MSE, PSNR, and Entropy, as compared to the existing CLAHE method only, the proposed methodology has a lower MSE signifying lower error, a higher PSNR representing a lower amount of distortion, and higher information entropy which indicates higher obtained information. Furthermore, the implementation of the proposed approach is applied to 6000 X-ray images before deep learning classification modeling, which significantly improved from 50% to 78% validation accuracy. Therefore, the proposed method improves the image enhancement methodology and can substantially assist in diagnosing diseases.
Scopus ID: 85127555156
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-5
Conference Paper | Published: January 1, 2021
Abstract
Shrimp species belong to the class of Crustacea under order Decapoda under suborder Natantia. The shrimp species are characterized with semi-transparent body which grow up to more than 20 cm. In terms of economic impact, the shrimp industry is considered highly profitable based on the studies by WorldAtlas and Philippine Statistics Authority. Therefore, as part of the necessary better management principles (BMPs), shrimp growth should be monitored. However, for the shrimp length is typically measured by a manual tool like rulers or calipers which is known to be a tedious process most especially when large number of samples are considered. Hence, in this study, image processing via OpenCV was utilized to estimate the length of shrimp species. The performance of the image-based approach is compared with the manual measurement and yielded a relative percent error of 6.23%. Based on the results, it can be concluded that the image-based approach can be utilized to determine the shrimp length.
Scopus ID: 85127555504
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021)
Conference Paper | Published: January 1, 2021
Abstract
In a worldwide perspective of the most common cancer diseases, cervical cancer is ranked fourth most frequent whereas the worldwide mortality rate is at 54.56%. In the Philippines, the second leading site among women is cervical cancer next to breast cancer. Research shows that cervical cancer is one of the most treatable cancer forms if detected and managed early. Currently, the most reliable diagnosis and prevention method of cervical cancer is thru a regular testing via Pap Smear test and HPV vaccination being performed in hospitals worldwide. However, according to the Centers for Disease Control and Prevention in California, the cervical cancer screening rate of regular testing in hospitals went down significantly during the stay-at-home order by the government due to the COVID-19 pandemic. Also, there are limited research based on the behavior information in relation to cervical cancer risk prediction, but existing studies proves the possibility of the risk prediction based on behavior information. This paper presents an Artificial Neural Network-based model for early cervical cancer risk detection based on behavior information. The neural network was trained using scaled conjugate gradient back propagation. The system showed 98% overall correctness in early cervical cancer risk prediction.
Scopus ID: 85104983562
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-6
Conference Paper | Published: December 3, 2020
Abstract
Controlled environment agriculture are being developed with the purpose of increasing production yield in farms. For optimal yield, it is very important to have an understanding about the relationship of environmental factors such as radiation, temperature, nutrients, water, and in relation with the growth state of the crop. Growth monitoring of cherry tomato crops in traditional methods are extremely labor-intensive, destructive, and costly in terms of time and money. Thus, application of computer vision has become an area of interest in the study of monitoring tomatoes' growth. In this study, image processing techniques are employed to identify the growth stage of cherry tomato as fruiting, flowering, and leafing stage. Confusion matrix with True Positive rate and False negative rate, and ROC are used to evaluate the decision support system developed. Experimental results show a high performance in determining the growth stage of test cherry tomato images.
Scopus ID: 85105008189
2020 IEEE 12th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2020), pp. 1-5
Conference Paper | Published: December 3, 2020
Abstract
Diabetes is a disease which chronic in nature, which is caused by an elevated blood sugar (or blood glucose) level. The metabolic disease is linked to several potential serious organ complications including nerves, kidneys, eyes, blood vessels, and the heart. According to the International Diabetes Federation, in 2019, about 2 million deaths were recorded worldwide due to diabetes. Furthermore, according to Philippine Statistics Authority (PSA), Diabetes Mellitus is considered as the fifth main cause of in the Philippines in the past years and in a 2015 study, about 1.7 million Filipinos are still undiagnosed of diabetes. Therefore, several machine learning-based techniques were developed for diabetes risk prediction. However, these works have yet to utilize artificial neural networks using the symptom information of suspected diabetic patients. This research paper demonstrated an ANN-based diabetes risk classification based on the symptom information of patients. The scaled conjugate gradient backpropagation technique was utilized for neural network training process. The classification system showed 99.2% overall correctness in determining the likelihood of diabetes.
Scopus ID: 85084746978
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-5
Conference Paper | Published: November 1, 2019
Abstract
Shrimp farming is a highly profitable business in the aquaculture industry. The farming profitability can be achieved by the implementation of better management practices in conjunction with optimal shrimp feed management and growth monitoring. Manual measurement for shrimp growth on a large population is a tedious and difficult task. Underfeeding results to lower growth rate, and overfeeding results to environmental pollution. Automated, continuous, and non-invasive methods therefore such as computer vision are being increasingly employed. However, existing researches of vision-based measurement of growth parameters are not yet incorporated to shrimp feed management. This paper presented an Artificial Neural Network-based decision support system of classifying feed type whether starter, grower or finisher using area, length and weight derived from image processing techniques. The neural network was trained using scaled conjugate gradient back propagation. The decision support system exhibited promising results in feed type classification.