Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG19

Modern industry requires modern solutions for monitoring the automatic production of goods. Smart monitoring of the functionality of the mechanical parts of technology systems or machines is mandatory for a fully automatic production process. Although Deep Learning has been advancing, allowing for real-time object detection and other tasks, little has been investigated about the effectiveness of specially designed Convolutional Neural Networks for defect detection and industrial object recognition. In the particular study, we employed six publically available industrial-related datasets containing defect materials and industrial tools or engine parts, aiming to develop a specialized model for pattern recognition. Motivated by the recent success of the Virtual Geometry Group (VGG) network, we propose a modified version of it, called Multipath VGG19, which allows for more local and global feature extraction, while the extra features are fused via concatenation. The experiments verified the effectiveness of MVGG19 over the traditional VGG19. Specifically, top classification performance was achieved in five of the six image datasets, while the average classification improvement was 6.95%.

State Space Advanced Fuzzy Cognitive Map approach for automatic and non Invasive diagnosis of Coronary Artery Disease

Purpose: In this study, the recently emerged advances in Fuzzy Cognitive Maps (FCM) are investigated and employed, for achieving the automatic and non-invasive diagnosis of Coronary Artery Disease (CAD). Methods: A Computer-Aided Diagnostic model for the acceptable and non-invasive prediction of CAD using the State Space Advanced FCM (AFCM) approach is proposed. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the system and the interpretability of the decision mechanism. The proposed method is tested utilizing a CAD dataset from the Laboratory of Nuclear Medicine of the University of Patras. More specifically, two architectures of AFCMs are designed, and different parameter testing is performed. Furthermore, the proposed AFCMs, which are based on the new equations proposed recently, are compared with the traditional FCM approach. Results: The experiments highlight the effectiveness of the AFCM approach and the new equations over the traditional approach, which obtained an accuracy of 78.21%, achieving an increase of seven percent (+7%) on the classification task, and obtaining 85.47% accuracy. Conclusions: It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease. Conclusions and future research related to recent pandemic of coronavirus are provided.

Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks

In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this experiment is a collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included. The data was collected from the available X-Ray images on public medical repositories. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.

Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating

Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification. For the experiments, we utilize PET/CT images obtained from the Laboratory of Nuclear Medicine of the University of Patras, and the publically available database called Lung Image Database Consortium Image Collection (LIDC-IDRI). Furthermore, we apply simple data augmentation to generate new instances and to inspect the performance of the developed networks. Classification accuracy of 91% and 93% on the PET/CT dataset and on a selection of nodule images form the LIDC-IDRI dataset, is achieved accordingly. The results demonstrate that CNNs are a trustworth method for nodule classification. Also, the experiment confirms that data augmentation enhances the robustness of the CNNs.