Colorectal cancer (CRC) is the third most common cause of cancer-related deaths in the United States and is anticipated to cause another 52,580 deaths in 2023. The standard medical procedure for screening and treating colorectal disease is a colonoscopy. By effectively examining the colonoscopy to identify precancerous polyps early and remove them before they become cancerous, CRC mortality can be lowered significantly. Manual colonoscopy examination for precancerous polyps detection is time-consuming, tedious, and prone to human error. Automatic segmentation and examination could be fast and practical; however, existing automated methods fail to attain adequate accuracy in polyps segmentation. Moreover, these methods do not assess the risk of detected polyps. In this paper, we proposed an autonomous CRC screening method to detect polyps and assess their potential threats. The proposed method utilized DoubleU-Net for polyps segmentation and Vision Transformer (ViT) for classifying them based on their risks. The proposed method has achieved a mean dice-coefficient of 0.834 and 0.956 in segmentation for the Endotech challenge and Kvasir-SEG dataset, accordingly, outperforming the existing state-of-the-art polyps segmentation. Then, this method classified the segmented polyps as hyper-plastic or adenomatous with 99% test accuracy.

Bangladesh is undertaking a major transformation towards digitalization in every sector, and healthcare is no exception. Digitalization of the health sector is expected to improve healthcare services while reducing human effort and ensuring the satisfaction of patients and health professionals. However, for practical and successful digitalization, it is necessary to understand the perceptions of health professionals. Therefore, we conducted a cross-sectional survey in Bangladesh to investigate health professionals’ perceptions in relation to various socio–demographic variables such as age, gender, location, profession and institution. We also evaluated their competencies, as digital health-related competencies are required for digitalization. Additionally, we identified major digitalization challenges. Quantitative survey data were analyzed with Python Pandas, and qualitative data were classified using Valence-Aware Dictionary and Sentiment Reasoner (VADER). This study found significant relationships between age ${\chi }^2(12,N=701)=82.02,p<0.001$; location ${\chi }^2(4,N=701)=18.78,p<0.001$; and profession ${\chi }^2(16,N=701)=71.02,p<0.001$; with technical competency. These variables also have similar influences on psychological competency. According to VADER, 88.1% (583/701) of respondents have a positive outlook toward digitalization. The internal consistency of the survey was confirmed by Cronbach’s alpha score (0.746). This study assisted in developing a better understanding of how professionals perceive digitalization, categorizes professionals based on competency, and prioritizes the major digitalization challenges.

Traditionally, pathological analysis and diagnosis are performed by manually eyeballing glass-slide specimens under a microscope by an expert. The whole slide image (WSI) is the digital specimen produced from the glass slide. WSI enabled specimens to be observed on a computer screen and led to computational pathology where computer vision and artificial intelligence are utilized for automated analysis and diagnosis. With the current computational advancement, the entire WSI can be analyzed autonomously without human supervision. However, the analysis could fail or lead to wrong diagnosis if the WSI is affected by tissue artifacts such as tissue fold or air bubbles depending on the severity. Existing artifact detection methods rely on experts for severity assessment to eliminate artifact-affected regions from the analysis. This process is time-consuming, exhausting and undermines the goal of automated analysis or removal of artifacts without evaluating their severity, which could result in the loss of diagnostically important data. Therefore, it is necessary to detect artifacts and then assess their severity automatically. In this paper, we propose a system that incorporates severity evaluation with artifact detection utilizing convolutional neural networks (CNN). The proposed system uses DoubleUNet to segment artifacts and an ensemble network of six fine-tuned CNN models to determine severity. This method outperformed current state-of-the-art in accuracy by 9% for artifact segmentation and achieved a strong correlation of 97% with the pathologist’s evaluation for severity assessment. The robustness of the system was demonstrated using our proposed heterogeneous dataset and practical usability was ensured by integrating it with an automated analysis system.

Automated object identification has seen significant progress during the last decade with close to human-level accuracy, aided by deep learning methods. With the rapid rise of obesity and other lifestyle-related diseases worldwide, the availability of fast, automated, and reliable image-based food calorie estimation is becoming a necessity. With the help of a deep learning-based automated object identification system, it is possible to introduce accurate and intelligent solutions in the form of a mobile app. However, for these kind of applications, processing speed is an important concern as the images should be processed in real time. Although plenty of studies have been conducted that focus on food image detection-based calorie estimation, there is still a lack of an image-driven, lightweight, fast, and reliable food calorie estimation system. In this paper, we propose a method based on the parameter-optimized Convolution Neural Networks (CNN) for detecting food images of regular meals using a handheld camera. Once identification process of the food items are complete, the corresponding calories and nutritional facts can be calculated using prior knowledge about the food class. Through our findings, we demonstrate that our proposed approach ensures high accuracy and can significantly simplify the existing manual calorie estimation procedures by converting them into a real-time automated process.

Assessment and monitoring of surface water quality are essential for food security, public health, and ecosystem protection. Although water quality monitoring is a known phenomenon, little effort has been made to offer a comprehensive and harmonized dataset for surface water at the global scale. This study presents a comprehensive surface water quality dataset that preserves spatio-temporal variability, integrity, consistency, and depth of the data to facilitate empirical and data-driven evaluation, prediction, and forecasting. The dataset is assembled from a range of sources, including regional and global water quality databases, water management organizations, and individual research projects from five prominent countries in the world, e.g., the USA, Canada, Ireland, England, and China. The resulting dataset consists of 2.82 million measurements of eight water quality parameters that span 1940 - 2023. This dataset can support meta-analysis of water quality models and can facilitate Machine Learning (ML) based data and model-driven investigation of the spatial and temporal drivers and patterns of surface water quality at a cross-regional to global scale.

Aquatic vegetation species are declining gradually, posing a threat to the stability of aquatic ecosystems. The decline can be controlled with proper monitoring and mapping of the species for effective conservation and management. The Unmanned Ariel Vehicle (UAV) aka Drone can be deployed to comprehensively capture large area of water bodies for effective mapping and monitoring. This study developed the AqUavplant dataset consisting of 197 high resolution (3840px × 2160px, 4K) images of 31 aquatic plant species collected from nine different sites in Bangladesh. The DJI Mavic 3 Pro triple-camera professional drone is used with a ground sampling distance (GSD) value of 0.04-0.05 cm/px for optimal image collection without losing detail. The dataset is complemented with binary and multiclass semantic segmentation mask to facilitate ML based model development for automatic plant mapping. The dataset can be used to detect the diversity of indigenous and invasive species, monitor plant growth and diseases, measure the growth ratio to preserve biodiversity, and prevent extinction.