Intelligent Analysis of Computer-Aided Garment Design CAD Based on Virtual Reality Model for Surplus Fabric Removal and Reuse without Segmentation of Cutting Pieces

For the purpose of improving the efficiency of garment design, the computer-aided garment design virtual reality (VR) model for surplus fabric removal and reuse without segmentation of cutting pieces is analyzed in this paper to provide the architecture of the computer-aided garment design CAD system. The form of dividing the garment into multiple types of nonsegmented pieces is adopted so that each nonsegmented piece stands for a complete design element unit. Based on this structure, the computer analysis of garment design based on CAD can be connected at a deeper level, which will not only improve the design efficiency of new garments but also reduce the design time at the client terminal and enhance the quality of the design. Through the experimental operation of prototypes, it is verified that the intelligent system proposed in this paper can implement the design of prototypes quickly and effectively.

The Role of Structure MRI in Diagnosing Autism

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

Current Advances in Computational biology of Lung Cancer

Lung cancer is the main basis of cancer death amongst men and women, making up almost 25% of the world’s total cancer deaths. Lung cancer described for nearly 1.6 million deaths in 2012 and 1.80 million deaths in 2020. Small cell lung cancer and non-small-cell lung cancer are the two key categories of Lung cancer. The signs of lung cancer include hemoptysis, weight loss, shortness of breath and chest pain. Lung cancer treated by chemotherapy, surgery and CT scan. In this review paper, one of the most crucial zones aiming lung cancer diagnosis has been discussed. Computer-aided diagnosis (CAD) systems adapted for lung cancer can increase the patients’ survival chances. A typical CAD system for lung cancer functions in the fields of lung segmentation, detecting lung nodules and the diagnosis of the nodules as benign or malignant. CAD systems for lung cancer are examined in a huge number of research case studies. CAD system steps and outlining of inhibitor genes at molecular level is being discussed. An insight into multi-omics and molecular dynamics simulations is also given in this paper.

Melanoma Detection in Dermoscopic Images Using a Cellular Automata Classifier

Any cancer type is one of the leading death causes around the world. Skin cancer is a condition where malignant cells are formed in the tissues of the skin, such as melanoma, known as the most aggressive and deadly skin cancer type. The mortality rates of melanoma are associated with its high potential for metastasis in later stages, spreading to other body sites such as the lungs, bones, or the brain. Thus, early detection and diagnosis are closely related to survival rates. Computer Aided Design (CAD) systems carry out a pre-diagnosis of a skin lesion based on clinical criteria or global patterns associated with its structure. A CAD system is essentially composed by three modules: (i) lesion segmentation, (ii) feature extraction, and (iii) classification. In this work, a methodology is proposed for a CAD system development that detects global patterns using texture descriptors based on statistical measurements that allow melanoma detection from dermoscopic images. Image analysis was carried out using spatial domain methods, statistical measurements were used for feature extraction, and a classifier based on cellular automata (ACA) was used for classification. The proposed model was applied to dermoscopic images obtained from the PH2 database, and it was compared with other models using accuracy, sensitivity, and specificity as metrics. With the proposed model, values of 0.978, 0.944, and 0.987 of accuracy, sensitivity and specificity, respectively, were obtained. The results of the evaluated metrics show that the proposed method is more effective than other state-of-the-art methods for melanoma detection in dermoscopic images.

Two Routes to Trust Calibration

Trust calibration takes place prior to and during system interaction along the available information. In an online study N = 519 participants were introduced to a conditionally automated driving (CAD) system and received different a priori information about the automation's reliability (low vs high) and brand of the CAD system (below average vs average vs above average reputation). Trust was measured three times during the study. Additionally, need for cognition (NFC) and other personality traits were assessed. Both heuristic brand information and reliability information influenced trust in automation. In line with the Elaboration Likelihood Model (ELM), participants with high NFC relied on the reliability information more than those with lower NFC. In terms of personality traits, materialism, the regulatory focus and the perfect automation scheme predicted trust in automation. These findings show that a priori information can influence a driver's trust in CAD and that such information is interpreted individually.

Reliability of a computer-aided system in the evaluation of indeterminate ultrasound images of thyroid nodules

Introduction Computer-aided diagnostic (CAD) programs for malignancy risk stratification from ultrasound (US) imaging of thyroid nodules are being validated both experimentally and in real-world practice. However, they have not been tested for reliability in analyzing difficult or unclear images. Methods US images with indeterminate characteristics were evaluated by five observers with different experience in US examination and by a commercial CAD program. The nodules, on which the observers widely agreed, were considered concordant and, if there was little agreement, not concordant or difficult to assess. The diagnostic performance of the readers and the CAD program was calculated and compared in both groups of nodule images. Results In the group of concordant thyroid nodules (n = 37), the clinicians and the CAD system obtained similar levels of accuracy (77.0% vs 74.2%, respectively; P = 0.7) and no differences were found in sensitivity (SEN) (95.0% vs 87.5%, P = 0.2), specificity (SPE) (45.5 vs 49.4, respectively; P = 0.7), positive predictive value (PPV) (75.2% vs 77.7%, respectively; P = 0.8), nor negative predictive value (NPV) (85.6 vs 77.7, respectively; P = 0.3). When analyzing the non-concordant nodules (n = 43), the CAD system presented a decrease in accuracy of 4.2%, which was significantly lower than that observed by the experts (19.9%, P = 0.02). Conclusions Clinical observers are similar to the CAD system in the US assessment of the risk of thyroid nodules. However, the AI system for thyroid nodules AmCAD-UT® showed more reliability in the analysis of unclear or misleading images.

Improving Classification Accuracy of Pulmonary Nodules using Simplified Deep Neural Network

Background: Lung cancer is among the major causes of death in the world. Early detection of lung cancer is a major challenge. These encouraged the development of Computer-Aided Detection (CAD) system. Objectives: We designed a CAD system for performance improvement in detecting and classifying pulmonary nodules. Though the system will not replace radiologists, it will be helpful to them in order to accurately diagnose lung cancer. Methods: The architecture comprises of two steps, among which in the first step CT scans are pre-processed and the candidates are extracted using the positive and negative annotations provided along with the LUNA16 dataset, and the second step consists of three different neural networks for classifying the pulmonary nodules obtained from the first step. The models in the second step consist of 2D-Convolutional Neural Network (2D-CNN), Visual Geometry Group-16 (VGG-16) and simplified VGG-16, which independently classify pulmonary nodules. Results: The classification accuracies achieved for 2D-CNN, VGG-16 and simplified VGG-16 were 99.12%, 98.17% and 99.60%, respectively. Conclusion: The integration of deep learning techniques along with machine learning and image processing can serve as a good means of extracting pulmonary nodules and classifying them with improved accuracy. Based on these results, it can be concluded that the transfer learning concept will improve system performance. In addition, performance improves proper designing of the CAD system by considering the amount of dataset and the availability of computing power.

METHOD FOR CALCULATING CUTTING FORCES BY MODELING CAD WITH A SYSTEM OF GEOMETRIC CUTTING PARAMETERS WITH RADIAL MILLS

The accuracy of processing surfaces of a complex profile largely depends on the selected processing strategy, which will allow creating the same, within certain limits, power characteristics of the shaping process at the intervals of the programmed tool path. In this case, it becomes possible to include tuning modules in programs for CNC machines that form vector values of corrections in certain areas, as reactors for elastic deformations of the cutting process. Therefore, it is especially important to know the modulus and direction of the resulting cutting force vector, which does not necessarily coincide with the feed direction. The purpose of this work is to build a method for calculating cutting forces by modeling the geometric parameters of a cut with a CAD system, a cutter with a nonlinear generatrix. Solid modeling of the process is based on the Boolean operations of "intersection" and "subtraction" of 3D objects: the teeth of a radius cutter with a helical cutting edge and a workpiece "moving" at a feed rate. The tool for the implementation of this method is a software module created on the basis of API functions, the input data for which are: a 3D tool and a workpiece, the equation of the trajectory of its movement and the parameters of the infeed movement. Targeting API properties, the application makes it possible to simulate various trajectories, helical or trochoidal, when machining complex surfaces. In the future, it is possible to take into account the plastic deformation processes in the chip formation zone in the model by connecting external modules. In the course of the conducted research on milling with radial end mills with a helical cutting edge, when two or more teeth are within the arc of contact, it was determined by 3D modeling how much thickness and width the layer cuts off each of the teeth during the feed per revolution. Consequently, in the process of shaping, normal and tangential cutting forces, which are different in direction and modulus, are present as a function of the angle of rotation of the cutter. Therefore, the concept of "circumferential force on the cutter", accepted in the theory of cutting, as a certain constant component of the process, can introduce an error when considering the causes of the excitation mechanism of vibrations of different nature that arise in the processing zone.