Search Results (4)

A point cloud is a representation of objects or scenes utilising unordered points comprising 3D positions and attributes. The ability of point clouds to mimic natural forms has gained significant attention from diverse applied fields, such as virtual reality and augmented reality. However, the point cloud, especially those representing dynamic scenes or objects in motion, must be compressed efficiently due to its huge data volume. The latest video-based point cloud compression (V-PCC) standard for dynamic point clouds divides the 3D point cloud into many patches using computationally expensive normal estimation, segmentation, and refinement. The patches are projected onto a 2D plane to apply existing video coding techniques. This process often results in losing proximity information and some original points. This loss induces artefacts that adversely affect user perception. The proposed method segments dynamic point clouds based on shape similarity and occlusion before patch generation. This segmentation strategy helps maintain the points’ proximity and retain more original points by exploiting the density and occlusion of the points. The experimental results establish that the proposed method significantly outperforms the V-PCC standard and other relevant methods regarding rate–distortion performance and subjective quality testing for both geometric and texture data of several benchmark video sequences. Full article

Screening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening. Full article

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective in detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the “black-box” nature of deep learning models. Additionally, most lung nodules visible on chest X-rays are benign; therefore, the narrow task of computer vision-based lung nodule detection cannot be equated to automated lung cancer detection. Addressing both concerns, this study introduces a novel hybrid deep learning and decision tree-based computer vision model, which presents lung cancer malignancy predictions as interpretable decision trees. The deep learning component of this process is trained using a large publicly available dataset on pathological biomarkers associated with lung cancer. These models are then used to inference biomarker scores for chest X-ray images from two independent data sets, for which malignancy metadata is available. Next, multi-variate predictive models were mined by fitting shallow decision trees to the malignancy stratified datasets and interrogating a range of metrics to determine the best model. The best decision tree model achieved sensitivity and specificity of 86.7% and 80.0%, respectively, with a positive predictive value of 92.9%. Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists. Full article

Reasonable use of agricultural machinery has an extraordinary potential for poverty alleviation by increasing land and labor productivity in Thailand, Vietnam, and even in Bangladesh. This study was conducted under a program entitled “Agriculture Mechanization, Agro-Processing, Value addition and Export Market Development in Thailand and Vietnam from 1–14 November, 20I9” from the Ministry of Agriculture, Bangladesh. In all three distinct nations, farming activities represent a significant area of activity and remains the biggest wellspring of agricultural business. About 10.5% of Thailand’s, 21.5% of Vietnam’s, and 14.23% of Bangladesh’s GDP come from agriculture. For sustainable development, it is essential to modernize agriculture through the mechanization of its operations, which is therefore inevitable in the studied countries. Thailand’s government started mechanization in 1891 with the import of steam-powered tractor and rotary hoes. Since then the country has witnessed several milestones in the course of mechanization development. The focal plain agro-ecological zone of the state is the maximum and almost fully modernized area. As of now, there are two methods of practicing farming apparatus use: as a proprietor and/or through custom renting provision which coincides with Vietnam and Bangladesh. Historically, mechanization patterns in Vietnam can been described by tillage machinery with associated implement equipment use preceding 1975. This was non-linear, followed by a decreasing trend during the 80s prior to recovery during the 90s, with significant disparities in implementation status across the areas. In 2018, the number of tillage implements and harvesters was boosted about 1.6 and 25.6 times, respectively compared with 2006. The percentage of machinery use in soil tillage operation is 80% of the whole territory of cultivable land in Vietnam, compared to about 90% in Bangladesh and 100% in Thailand. Mechanization in Bangladesh started before independence with the importation of 2-wheel tractors and irrigation pumps in the last part of the 1960s as part of ‘Green Revolution’ activities. To continue this momentum, the Bangladesh Government permitted the continuation of agricultural machinery importation after later autonomy. Machinery use in different agricultural activities has increased in recent years in the areas of irrigation, land preparation, intercultural operation, and threshing. Though its degree of advancement is by and large still quite low contrasted with other South Asian nations, it is noticeable that the most recent two decades, the pace of mechanization has increased rapidly with the increase of mechanical power use in farm activities. The use of farm machinery in rice cultivation has been the most amazing when contrasted with different crops in these three nations. A clear comparison has been given in the paper, which aims to help researchers and policymakers take necessary measures. Full article