This case report shows that pleural empyema limits the diagnostic significance of imaging techniques. Hereafter, we present the case of an 82-year-old patient with primary pericardial mesothelioma, which was veiled by a pleural empyema. The patient met the typical triad of signs of heart failure (dyspnea, lower leg oedema), pericardial effusion, and pericarditis. Echocardiography in the identification of pericardial mesotheliomas is low. In this case, the cardiac function could be imaged well, but the tumor could not be imaged. The CT showed a pericardial effusion and a pleural effusion. Here, the tumor could not be diagnosed either. Only the operation led to diagnosis.

For a range of medical analysis applications, the localization of brain tumors and brain tumor segmentation from magnetic resonance imaging (MRI) are challenging yet critical jobs. Many recent studies have included four modalities: i.e., T1, T1c, T2 & FLAIR, it is because every tumor causing area can be detailed examined by each of these brain imaging modalities. Although the BRATS 2018 datasets give impressive segmentation results, the results are still more complex and need more testing and more training. That’s why this paper recommends operated pre-processing strategies on a small part of an image except for a full image because that’s how an effective and flexible segmented system of brain tumor can be created. In the first phase, an ensemble classification model is developed using different classifiers such as decision tree, SVM, KNN etc. to classify an image into the tumor and non-tumor class by using the strategy of using a small section can completely solve the over-fitting problems and reduces the processing time in a model of YOLO object detector using inceptionv3 CNN features. The second stage is to recommend an efficient and basic Cascade CNN (C-ConvNet/C-CNN), as we deal with a tiny segment of the brain image in each and every slice. In two independent ways, the Cascade-Convolutional Neutral Network model extracts learnable features. On the dataset of BRATS 2018, BRATS 2019 and BRATS 2020, the extensive experimental task has been carried out on the proposed tumor localization framework.: the IoU score achieved of three datasets are 97%, 98% and 100%. Other qualitative evaluations & quantitative evaluations are discussed and presented in the manuscript in detail.

The paved cracks are one of the major concerns for scientists and engineers in road maintenance and damage evaluation study. Digital image processing applications have been applied in road surface inspection, classification, and decomposition of paved roads. This paper has tested and proposed the process to evaluate the road cracks and their possible solution as we know that the key issues for analysis are enhancement and segmentation of image along with edge detection to attain the promising results we have gained and discussed under the heading of simulations for the experimental and numerical of crack detection. Using MATLAB, we examine the various gray-level image using better techniques based on their computational capability. The method is based upon one of the histogram modification techniques, which is coupled with the segmentation method and the crack edge detection. At last, three feature methods are used, namely, Harris, MSERF, and SURF, to wind up our research.

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Due to their low power consumption and limited computing power, Internet of Things (IoT) devices are difficult to secure. Moreover, the rapid growth of IoT devices in homes increases the risk of cyber-attacks. Intrusion detection systems (IDS) are commonly employed to prevent cyberattacks. These systems detect incoming attacks and instantly notify users to allow for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning techniques, however, these efforts have been unsuccessful. In this paper, we propose two deep learning models to automatically detect various types of intrusion attacks in IoT networks. Specifically, we experimentally evaluate the use of two Convolutional Neural Networks (CNN) to detect nine distinct types of attacks listed in the NF-UNSW-NB15-v2 dataset. To accomplish this goal, the network stream data were initially converted to twodimensional images, which were then used to train the neural network models. We also propose two baseline models to demonstrate the performance of the proposed models. Generally, both models achieve high accuracy in detecting the majority of these nine attacks.