A Framework for Crop Disease Detection Using Feature Fusion Method

Crop disease detection methods vary from traditional machine learning, which uses Hand-Crafted Features (HCF) to the current deep learning techniques that utilize deep features. In this study, a hybrid framework is designed for crop disease detection using feature fusion. Convolutional Neural Network (CNN) is used for high level features that are fused with HCF. Cepstral coefficients of RGB images are presented as one of the features along with the other popular HCF. The proposed hybrid model is tested on the whole leaf images and also on the image patches which have individual lesions. The experimental results give an enhanced performance with a classification accuracy of 99.93% for the whole leaf images and 99.74% for the images with individual lesions. The proposed model also shows a significant improvement in comparison to the state-of-art techniques. The improved results show the prominence of feature fusion and establish cepstral coefficients as a pertinent feature for crop disease detection

Patella radiograph image texture: The correlation with lumbar spine bone mineral density values

Osteoporosis is a common metabolic disease that is frequently overlooked. This disease primarily affects adult women and causes bone thinness and fragility, which leads to fractures. DXA (Dual Energy X-ray Absorptiometry) is used to diagnose osteoporosis by measuring bone mineral density. These devices are expensive and not widely available for treatment. This study aimed to find a correlation between the texture value of an image of the patellar bone and the density of the lumbar spine, which can then be used to detect osteoporosis. This study's sample size was 19 subjects, and their bone mineral density (BMD) was measured using DXA. An X-ray was then taken to obtain an image of the genu bone. The stages of the research are as follows: 1) preparing the X-ray image of the bone; 2) determining the image texture value method of gray level co-occurrence matrix 3) investigating the relationship between texture values and BMD in the lumbar spine. The correlation test results revealed a statistically significant correlation between the texture value and the BMD of the lumbar spine for the correlation and variance characteristics (P less than 0.05). As a result, the value of the texture of the image of the patella bone can be used to detect osteoporosis

KNOWLEDGE FUSION IN ALGORITHMS FOR MEDICAL IMAGE ANALYSIS

Medical imaging is one of the primary modalities used for clinical diagnosis and treatment planning. Building up a reliable automatic system to assist clinicians read the enormous amount of images benefits the efficiency and accuracy in general clinical trail. Recently deep learning techniques have been widely applied on medical images, but for applications in real clinical scenario, the accuracy, robustness, interpretability of those algorithms requires further validation. In this dissertation, we introduce different strategies of knowledge fusion for improving current approaches in various tasks in medical image analysis. (i) To improve the robustness of segmentation algorithm, we propose to learn the shape prior for organ segmentation and apply it for automatic quality assessment. (ii) To detect pancreatic lesion with patient-level label only, we propose to extract shape and texture information from CT scans and combine them with a fusion network. (iii) In image registration, semantic information is important yet hard to obtain. We propose two methods for introducing semantic knowledge without the need of segmentation label. The first one designs a joint framework for registration synthesis and segmentation to share knowledge between different tasks. The second one introduces unsupervised semantic embedding to improve regular registration framework. (iv) To reduce the false positives in tumor detection task, we propose a hybrid feature engineering system extracting features of the tumor candidates from various perspectives and merging them in the decision stage

Advances in Artificial Intelligence: Models, Optimization, and Machine Learning

The present book contains all the articles accepted and published in the Special Issue “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning” of the MDPI Mathematics journal, which covers a wide range of topics connected to the theory and applications of artificial intelligence and its subfields. These topics include, among others, deep learning and classic machine learning algorithms, neural modelling, architectures and learning algorithms, biologically inspired optimization algorithms, algorithms for autonomous driving, probabilistic models and Bayesian reasoning, intelligent agents and multiagent systems. We hope that the scientific results presented in this book will serve as valuable sources of documentation and inspiration for anyone willing to pursue research in artificial intelligence, machine learning and their widespread applications

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome