Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods

Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms. Although segmentation is the most widely investigated medical image processing task, the various challenges have been organized to focus only on specific clinical tasks. We organized the Medical Segmentation Decathlon (MSD)—a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities to investigate the hypothesis that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. MSD results confirmed this hypothesis, moreover, MSD winner continued generalizing well to a wide range of other clinical problems for the next two years. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to scientists that are not versed in AI model training

A Hybrid COVID-19 Detection Model Using an Improved Marine Predators Algorithm and a Ranking-Based Diversity Reduction Strategy

Many countries are challenged by the medical resources required for COVID-19 detection which necessitates the development of a low-cost, rapid tool to detect and diagnose the virus effectively for a large numbers of tests. Although a chest X-Ray scan is a useful candidate tool the images generated by the scans must be analyzed accurately and quickly if large numbers of tests are to be processed. COVID-19 causes bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. In this work, we aim to extract rapidly from chest X-Ray images the similar small regions that may contain the identifying features of COVID-19. This paper therefore proposes a hybrid COVID-19 detection model based on an improved marine predators algorithm (IMPA) for X-Ray image segmentation. The ranking-based diversity reduction (RDR) strategy is used to enhance the performance of the IMPA to reach better solutions in fewer iterations. RDR works on finding the particles that couldn't find better solutions within a consecutive number of iterations, and then moving those particles towards the best solutions so far. The performance of IMPA has been validated on nine chest X-Ray images with threshold levels between 10 and 100 and compared with five state-of-art algorithms: equilibrium optimizer (EO), whale optimization algorithm (WOA), sine cosine algorithm (SCA), Harris-hawks algorithm (HHA), and salp swarm algorithms (SSA). The experimental results demonstrate that the proposed hybrid model outperforms all other algorithms for a range of metrics. In addition, the performance of our proposed model was convergent on all numbers of thresholds level in the Structured Similarity Index Metric (SSIM) and Universal Quality Index (UQI) metrics.</p

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

Application of metaheuristic optimization algorithms for image registration in mobile robot visual control

Visual Servoing (VS) of a mobile robot requires advanced digital image processing, and one of the techniques especially fitting for this complex task is Image Registration (IR). In general, IR involves the geometrical alignment of images, and it can be viewed as an optimization problem. Therefore, we propose Metaheuristic Optimization Algorithms (MOA) for IR in VS of a mobile robot. The comprehensive comparison study of three state-of-the-art MOA, namely the Slime Mould Algorithm (SMA), Harris Hawks Optimizer (HHO), and Whale Optimization Algorithm (WOA) is presented. The previously mentioned MOA used for IR are evaluated on 12 pairs of stereo images obtained by a mobile robot stereo vision system in a laboratory model of a manufacturing environment. The MATLAB software package is used for the implementation of the considered optimization algorithms. Acquired experimental results show that SMA outperforms HHO and WOA, while all three algorithms perform satisfactory alignment of images captured from various mobile robot poses

A Clinical Prognostic Framework for Classifying Severe Liver Disorders (SLDs) and Lungs’ Vulnerability to Virus

Most severe liver diseases (SLDs) are attributed to increased risk for cancer, and cirrhosis, through which the manifestation of fibrotic tissues and scars tends to affect liver function The role of liver is indispensable, as inner organ performing services that ranges from metabolism, immune guide, energy producer and digestive aid, just to mention a few. Prevalence of classification problem and the need for automated prognosis is the continual drive to apply data mining techniques and/or machine learning algorithms in medical diagnosis and clinical support systems. Computational scientists and researchers in the field of artificial intelligence have recorded notable efforts with existing methods/models for diagnosis or prognosis, yet their effectiveness and functional performance is not without drawback due to ambiguity of medical information and selected features in patients’ data to tell the future course. In this paper, a novel hybridized machine learning model was provided (Fuzzy c-BC) for clinical classification of Severe Liver Disorders (SLDs) and to determine Lungs Vulnerability (LV) to virus; by incorporating individual strength of fuzzy cluster means (FCM) and naive Bayes classifier (NBC) for projecting future course of every categorized liver disease (LD) and its implication to aggravate lungs infection if preventive measures are not taken in timely manner

Human treelike tubular structure segmentation: A comprehensive review and future perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed