Application for White Spot Syndrome Virus (WSSV) Monitoring using Edge Machine Learning

The aquaculture industry, strongly reliant on shrimp exports, faces challenges due to viral infections like the White Spot Syndrome Virus (WSSV) that severely impact output yields. In this context, computer vision can play a significant role in identifying features not immediately evident to skilled or untrained eyes, potentially reducing the time required to report WSSV infections. In this study, the challenge of limited data for WSSV recognition was addressed. A mobile application dedicated to data collection and monitoring was developed to facilitate the creation of an image dataset to train a WSSV recognition model and improve country-wide disease surveillance. The study also includes a thorough analysis of WSSV recognition to address the challenge of imbalanced learning and on-device inference. The models explored, MobileNetV3-Small and EfficientNetV2-B0, gained an F1-Score of 0.72 and 0.99 respectively. The saliency heatmaps of both models were also observed to uncover the "black-box" nature of these models and to gain insight as to what features in the images are most important in making a prediction. These results highlight the effectiveness and limitations of using models designed for resource-constrained devices and balancing their performance in accurately recognizing WSSV, providing valuable information and direction in the use of computer vision in this domain.Comment: 6 pages, 7 figures, conferenc

Baybayin Character Instance Detection

The Philippine Government recently passed the "National Writing System Act," which promotes using Baybayin in Philippine texts. In support of this effort to promote the use of Baybayin, we present a computer vision system which can aid individuals who cannot easily read Baybayin script. In this paper, we survey the existing methods of identifying Baybayin scripts using computer vision and machine learning techniques and discuss their capabilities and limitations. Further, we propose a Baybayin Optical Character Instance Segmentation and Classification model using state-of-the-art Convolutional Neural Networks (CNNs) that detect Baybayin character instances in an image then outputs the Latin alphabet counterparts of each character instance in the image. Most existing systems are limited to character-level image classification and often misclassify or not natively support characters with diacritics. In addition, these existing models often have specific input requirements that limit it to classifying Baybayin text in a controlled setting, such as limitations in clarity and contrast, among others. To our knowledge, our proposed method is the first end-to-end character instance detection model for Baybayin, achieving a mAP50 score of 93.30%, mAP50-95 score of 80.50%, and F1-Score of 84.84%

Investigating biological feature detectors in simple pattern recognition towards complex saliency prediction tasks

The conventional convolution filter in deep architectures has proven its capability to extract semantic information from the input images and to use these in different visual tasks. For many researchers in computer vision, this raises the question, have pattern recognition models begun to converge on human performance? This thesis explores a new biologically-inspired feature detector for pattern recognition which learns via competition. We describe and exhaustively characterize our proposed alternative feature detector and compare this with the traditional convolution filter feature detector. Our experiments show the potential of the proposed feature detector and that its performance is at par with the performance of the convolution filter. Using the feature detector with more desirable result, we then design and propose a computational model for one of the primitive pattern recognition tasks of the visual system, the saliency map generation. The study provides a methodology for quantifying the contribution of the convolution filter in simple pattern recognition tasks and use this to benchmark our proposed competition-based feature detectors. Towards achieving an improved computational model for a complex prediction task of visual systems, we further use the biological feature detectors in extracting and incorporating emotion-evoking objects in saliency prediction

A computer assisted diagnosis system for the identification/auscultation of pulmonary pathologies

Statistics show that the primary cause of morbidity and mortality among Filipinos are pulmonary illnesses. These illnesses could have been prevented if detected and treated early. With the physicians medical knowledge and experience, early detection of possible common pulmonary diseases can be performed using a stethoscope. However, with the current physician-to-population ratio in the country, early detection of respiratory diseases may not be performed on most cases especially in the rural areas, causing even benign cases to lead to mortality. In this paper, we present the development of a system that classifies lung sound for possible pulmonary pathology.Using an electronic stethoscope, lung sounds were collected from healthy individuals and patients with common pulmonary problems for the developed systems training and evaluation. The collected data were pre-processed in order to remove mechanical and other external noises. Using Support Vector Machine (SVM) for modelling and classification, the developed system was able to achieve 100% identification of the normal lung sound from the adventitious lung sound, with an average cross-validation performance of 88%. The developed system, however, has low performance in classifying specific lung sounds, that is, normal vs. crackle vs. wheeze vs. ronchi, with an average accuracy of 61.42% and an average cross-validation performance of 90%

Measuring the contribution of filter bank layer to performance of convolutional neural networks

Object identification is essential in diverse automated applications such as in health, business, and national security. It relies on the ability of the image processing scheme to detect visual features under a wide variety of conditions such as the object rotation, translation and geometric transformation. Machine learning methods, in this case, play an important role in improving the object identification performance by resolving whether the extracted visual patterns are from the possibly distorted target object or not. In recent works, systems that employ a Convolutional Neural Network (CNN) as the primary pattern recognition scheme demonstrate superior performance over other object identification systems based on handpicked shape-based features. Several studies credit this to the invariance of CNN to small distortion and spatial translation which in turn is attributed to its filter bank layer or the convolution layer. However, there has been no study to carefully test this claim. Towards studying the source of CNN\u27s superior performance, a methodology is designed that tracks the CNN performance when spatial information for visual features (e.g. edges, corners and end points) are gradually removed. Using the MNIST dataset, results show that as the spatial correlation information among pixels is slowly decreased, the performance of the CNN in recognizing handwritten digits also correspondingly decreases. The drop in accuracy continues until the accuracy approximates the performance of the classifier that was obtained without the filter bank. Conducted using a more complex dataset consisting of images of land vehicles, a similar set of experiments show the same drop in classification performance as spatial information among pixels is slowly removed. © 2017 - IOS Press and the authors. All rights reserved

Which Filipino Students are Being Left Behind in Mathematics? Testing Machine Learning Models to Differentiate Lowest-Performing Filipino Students in Public and Private Schools in the 2018 PISA Mathematics Test

Filipino students performed poorly in the PISA 2018 mathematics assessment, with more than 50% obtaining scores below the lowest proficiency level. Students from public schools also performed worse compared to their private school counterparts. We used machine learning approaches, specifically binary classification methods, to model the variables that best identified the poor performing students (below Level 1) vs. better performing students (Levels 1 to 6) using the PISA data from a nationally representative sample of 15-year-old Filipino students. We analyzed data from students in private and public schools separately. Several binary classification methods were applied, and the best classification model for both private and public school groups was the Random Forest classifier. The 10 variables with the highest impact on the model were identified for the private and public school groups. Five variables were similarly important in the private and public school models. But there were other distinct variables that relate to students’ motivations, family, and school experiences that were important in identifying the poor performing students in each school type. The results are discussed in relation to the social and social cognitive experiences of students that relate to socioeconomic contexts that differ between public and private schools

Lung nodule detection and diagnosis using circle detection through plain radiographs

In this paper, we present a system that locates pulmonary nodules in digital chest radiographs through pattern recognition. Digital radiographs that are already diagnosed with lung nodules underwent histogram equalization in order to address varying illumination levels across different regions in the radiographs, and make the radiograph samples more comparable. Laplacian of Gaussian filtering is next applied in order to highlight the edges of pathological features like nodule-shaped blobs in each radiograph. Circular Hough Transform (CHT) was utilized in tandem with pixel-based image processing techniques in locating possible nodules. These system reports the count and sizes of the candidate nodules. We report an overall system accuracy of 73.33% when classifying digitized radiographs as either with nodules or without nodules

A simple lung sound enhancement for automatic identification of lung pathologies

Auscultation or lung sound analysis depends on the familiarity of the physician on detecting sound patterns. However, typical environment for auscultation are performed in rooms susceptible to different sounds such as vocal sound, ventilation machines and ambient noise, which may impede the subjective evaluation of the lung sounds. This paper presents a simple signal enhancement scheme for normal lung sounds in order to successfully extract the features which include the bandwidth, peak frequency and center frequency. The extracted features could be used in automatic detection and classifications of lung sound abnormalities of different. Results show that the enhanced signal has features in the 300 to 700 Hz range while the raw and denoised signals have features below 300 Hz. Listening test shows improved score in enhanced signals over scores on the raw and denoised signals with an average score of 1.3 over 1.03 in raw and 0.82 in denoised signals