Retinal Microvascular Feature Extraction Using Faster Region-based Convolutional Neural Network

Artificial Intelligence (AI) more specifically Deep Learning (DL) incorporating with image processing is being employed widely to solve different refractory problems by academia and industry from the ophthalmology discipline. The microvascular structure of the human retina shows remarkable abnormalities responding to different kinds of hazardous ophthalmic and cardiovascular diseases. The high dimensionality and complex hierarchical microvascular structure of the human retina, and random retinal image accumulation create enormous size data. This scenario is offering the challenge of understanding and managing retinal image data. The original input data need to be projected into output data which has a smaller number of features whilst as much as possible preserving its native information. This process is known as feature extraction. A recently introduced DL approach, Convolutional Neural Network (CNN), is dedicated to extracting and quantifying the complex hierarchical image features with more abstraction. The supervised CNN methods employ different algorithms that iteratively learn from data for analyzing data and predicting outcomes. The implementation of CNN methods has proved their efficiency in the identification, localization, and quantification of interesting retinal image features such as exudates, microaneurysms. These features are considered remarkable signs for detecting Diabetic Retinopathy (DR), Hypertensive Retinopathy (HR), and stroke. The quantitative features such as vessel widening and deviation in bifurcation angle are also relative to these diseases. The recently reported DL-based retinal image feature extraction methods are not dedicated to extracting retinal vessel segments from multiple locations of the retinal image. Extracting retinal vessel segments from the retinal image is important for vessel diameter and bifurcation angle quantification. Moreover, employing inappropriate image processing techniques at the pre-processing level can lead to poor system performance. This work is dedicated to developing an image processing-based AI method for retinal vessel extraction from retinal images. This thesis includes a brief explanation of the proposed method, Faster Region-based Convolutional Neural Network (Faster RCNN) for retinal image feature extraction. At the initial stage of this proposed method, fundamental image processing was used for retinal image preprocessing. The retinal images were taken from the different public databases to train, test, and validate the performance of this proposed method. This proposed method obtained 91.82% Mean Average Precision (mAP), 92.81% sensitivity, and 63.34% Positive Predictive Value (PPV). According to the performance analysis, it can be expected to integrate this proposed method into the ophthalmic diagnostic tools after further development, evaluation, and validatio

Feature Extraction of Retinal Microvasculature of Retinal Images

Image Processing, more generally digital image processing is one of the most widely used computer vision technology, especially in Biomedical engineering. Modern ophthalmology is directly dependent on this robust technology, digital image processing to find out the biomarkers analyzing the fundus eye images that are responsible for different kinds of life-threatening diseases like diabetic retinopathy, macular degeneration, hypertensive retinopathy, transient ischemic attack or sharp stroke and some other cardiovascular disease. The geometric features like vessel tortuosity, branching angles, vessel diameter and fractal dimension are considered as the biomarkers for the cardiovascular diseases mentioned above. Retinal vessel diameter widening has found as the early symptom of transient ischemic attack or sharp stroke. A succinct and meaningful review of the latest quantitative diagnostic methods that are developed employing the digital image analysis principles for measuring the remarkable features mainly the vessel diameter has been provided in the literature of this project. In this project, a completely new and computer-aided automatic method to measure the retinal vessel diameter employing the Euclidean Distance Transform technique has been developed. The proposed system measures the Euclidean Distance of the bright pixels exist on the Region of Interest (ROI). Further, the proposed system was evaluated on the High-Resolution Fundus Image Database (HRFID) and Retinal Vessel Image set for Estimation of Width (REVIEW) Database. The HRFID was used to evaluate the performance of the segmentation technique that was employed in this project and obtained 94.3% accuracy with 66.5% Sensitivity, 97.86% Specificity, 77.265 Positive Predictive Value (PPV) and 96.60% Negative Predictive Value (NPV). The Vascular Disease Image Set (VDIS) and Central Light Reflex Image Set (CLRIS) of REVIEW database were used to evaluate the overall system performance that measures the vessel diameter. The proposed system obtained 98.1% accuracy for the CLRIS and 97.7% accuracy for VDIS. With further evaluation, validation and enhancement of the method, it can be integrated into the clinical computer-aided diagnostic tool. The methodology and the evaluation results are explained in this report

Feature Extraction Algorithms of Retinal Microvasculature for Cost-Effective Medical Device

At present, chronic diseases such as stroke and diabetes mellitus continues to increase. In such medical conditions, if inappropriately treated, complications will easily occur such as visual morbidity, including blindness. According to the World Health Organization, as of 2010 worldwide, there are 39 million (13.6%) blind people due to visual morbidity related to chronic diseases. Therefore, this represent the magnitude of urgency needed to come up with technologies capable of preventing the unwanted complication (Mariotti, 2010). Digital image processing is one of the most remarkable advancing disciplines of computer visual image technology which is being widely employed in the modern biomedical imaging systems with increasing accuracy. This includes growing contributions of digital image processing in modern ophthalmic diagnostic systems. The human retina is the only location where blood vessels can be directly visualized non-invasively in vivo

Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh

The two economically important shrimp species in Bangladesh are the tiger shrimp, Penaeus monodon, and the brown shrimp, Metapenaeus monoceros. However, a continuous decline in the landing of these species from the industrial trawling made it critical to assess their stock biomass status to explore their response to the present degree of removal. Given the minimum data requirement and robustness, this study employed the depletion-based stock reduction analysis (DB-SRA) to assess these fisheries rigorously. For the industrial fishing zone (beyond the 40 m depth in the EEZ of Bangladesh), the estimated historic mean carrying capacity (K) was 5015 metric tons for the Penaeus monodon and 35,871 metric tons for Metapenaeus monoceros. The estimated overfishing limits (OFL), which were much smaller than the reported catches throughout the time series, indicate the overfishing status of these fisheries. As a result, the estimated biomass for the reference year (B2020) for both species was lower than BMSY, indicating that these fisheries are not producing MSY. Therefore, for the rebuilding and sustainable management of these stocks, this study recommended a catch limit of 100 metric tons for P. monodon and 750 metric tons for M. monoceros for the next ten years from biomass projections

Stock Assessment and Rebuilding of Two Major Shrimp Fisheries (Penaeus monodon and Metapenaeus monoceros) from the Industrial Fishing Zone of Bangladesh

The two economically important shrimp species in Bangladesh are the tiger shrimp, Penaeus monodon, and the brown shrimp, Metapenaeus monoceros. However, a continuous decline in the landing of these species from the industrial trawling made it critical to assess their stock biomass status to explore their response to the present degree of removal. Given the minimum data requirement and robustness, this study employed the depletion-based stock reduction analysis (DB-SRA) to assess these fisheries rigorously. For the industrial fishing zone (beyond the 40 m depth in the EEZ of Bangladesh), the estimated historic mean carrying capacity (K) was 5015 metric tons for the Penaeus monodon and 35,871 metric tons for Metapenaeus monoceros. The estimated overfishing limits (OFL), which were much smaller than the reported catches throughout the time series, indicate the overfishing status of these fisheries. As a result, the estimated biomass for the reference year (B2020) for both species was lower than BMSY, indicating that these fisheries are not producing MSY. Therefore, for the rebuilding and sustainable management of these stocks, this study recommended a catch limit of 100 metric tons for P. monodon and 750 metric tons for M. monoceros for the next ten years from biomass projections

BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP

Large Language Models (LLMs) have emerged as one of the most important breakthroughs in natural language processing (NLP) for their impressive skills in language generation and other language-specific tasks. Though LLMs have been evaluated in various tasks, mostly in English, they have not yet undergone thorough evaluation in under-resourced languages such as Bengali (Bangla). In this paper, we evaluate the performance of LLMs for the low-resourced Bangla language. We select various important and diverse Bangla NLP tasks, such as abstractive summarization, question answering, paraphrasing, natural language inference, text classification, and sentiment analysis for zero-shot evaluation with ChatGPT, LLaMA-2, and Claude-2 and compare the performance with state-of-the-art fine-tuned models. Our experimental results demonstrate an inferior performance of LLMs for different Bangla NLP tasks, calling for further effort to develop better understanding of LLMs in low-resource languages like Bangla.Comment: First two authors contributed equall

Length-Based Stock Assessment for the Data-Poor Bombay Duck Fishery from the Northern Bay of Bengal Coast, Bangladesh

The Bombay duck (Harpadon nehereus) forms the second-largest single-species marine fishery in Bangladesh and therefore has a significant impact on the local economy, providing employment, food, and nutrition to thousands of people. Despite the immense importance, this fishery has never been a priority for the relevant regulatory authorities. To enhance the sustainability of this fishery, an effective management policy based on the scientific evaluation of the current stock’s status is urgently required. Therefore, this study used three methodological approaches (traditional size structure-based stock assessment to reveal current exploitation status; the length-based spawning potential ratio (LB-SPR) to evaluate the stock’s spawning biomass; and Froese’s sustainability indicators for sustainable fishing) to conduct a thorough assessment of the Bombay duck stock to establish basic standards for the sustainable management of this fishery. The results revealed that this fishery is presently suffering from overexploitation and the stock’s spawning biomass (SPR = 8%) is below the limit reference point because of the juvenile-oriented fishing behavior of the fishery. Based on the outputs, this study recommended the mesh size regulation of the set bag nets (SBNs) (5 cm mesh size for the codend) to ensure not to catch immature fishes with a length equal to or smaller than 17.95 cm; and reduce the existing number of SBNs by half to reduce fishing pressure on the stock to ensure the sustainability of this fishery