The Optimal Model for Copy-Move Forgery Detection in Medical Images.

Background: Digital devices can easily forge medical images. Copy-move forgery detection (CMFD) in medical image has led to abuses in areas where access to advanced medical devices is unavailable. Forgery of the copy-move image directly affects the doctor's decision. The method discussed here is an optimal method for detecting medical image forgery. Methods: The proposed method is based on an evolutionary algorithm that can detect fake blocks well. In the first stage, the image is taken to the signal level with the help of a discrete cosine transform (DCT). It is then ready for segmentation by applying discrete wavelet transform (DWT). The low-low band of DWT, which has the most image properties, is divided into blocks. Each block is searched using the equilibrium optimization algorithm. The blocks are most likely to be selected, and the final image is generated. Results: The proposed method was evaluated based on three criteria of precision, recall, and F1 and obtained 90.07%, 92.34%, and 91.56%, respectively. It is superior to the methods studied on medical images. Conclusions: It concluded that our method for CMFD in the medical images was more accurate.

CFDMI-SEC: An optimal model for copy-move forgery detection of medical image using SIFT, EOM and CHM.

Image forgery is one of the issues that can create challenges for law enforcement. Digital devices can easily Copy-move images, forging medical photos. In the insurance industry, forensics, and sports, image forgery has become very common and has created problems. Copy-Move Forgery in Medical Images (CMFMI) has led to abuses in areas where access to advanced medical devices is unavailable. The proposed model (SEC) is a three-part model based on an evolutionary algorithm that can detect fake blocks well. In the first part, suspicious points are discovered with the help of the SIFT algorithm. In the second part, suspicious blocks are found using the equilibrium optimization algorithm. Finally, color histogram Matching (CHM) matches questionable points and blocks. The proposed method (SEC) was evaluated based on accuracy, recall, and F1 criteria, and 100, 97.00, and 98.47% were obtained for the fake medical images, respectively. Experimental results show robustness against different transformation and post-processing operations on medical images.

Minimizing outbreak through targeted blocking for disease control: a community-based approach using super-spreader node identification.

The COVID-19 pandemic has caused significant disruptions to the daily lives of individuals worldwide, with many losing their lives to the virus. Vaccination has been identified as a crucial strategy to combat the spread of a disease, but with a limited supply of vaccines, targeted blocking is becoming increasingly necessary. One such approach is to block a select group of individuals in the community to control the spread of the disease in its early stages. Therefore, in this paper, a method is proposed for solving this problem, based on the similarity between this issue and the problem of identifying super-spreader nodes. The proposed method attempts to select the minimum set of network nodes that, when removed, no large component remains in the network. To this end, the network is partitioned into various communities, and a method for limiting the spread of the disease to communities is proposed by blocking connecting nodes. Four real networks and four synthetics networks created using the LFR algorithm were used to evaluate the control of the disease by the selected set of nodes using each method, and the results obtained indicate better performance of the proposed method compared to other methods.

A multi-attribute method for ranking influential nodes in complex networks.

Calculating the importance of influential nodes and ranking them based on their diffusion power is one of the open issues and critical research fields in complex networks. It is essential to identify an attribute that can compute and rank the diffusion power of nodes with high accuracy, despite the plurality of nodes and many relationships between them. Most methods presented only use one structural attribute to capture the influence of individuals, which is not entirely accurate in most networks. The reason is that network structures are disparate, and these methods will be inefficient by altering the network. A possible solution is to use more than one attribute to examine the characteristics aspect and address the issue mentioned. Therefore, this study presents a method for identifying and ranking node's ability to spread information. The purpose of this study is to present a multi-attribute decision making approach for determining diffusion power and classification of nodes, which uses several local and semi-local attributes. Local and semi-local attributes with linear time complexity are used, considering different aspects of the network nodes. Evaluations performed on datasets of real networks demonstrate that the proposed method performs satisfactorily in allocating distinct ranks to nodes; moreover, as the infection rate of nodes increases, the accuracy of the proposed method increases.

Classifying tumor brain images using parallel deep learning algorithms.

One of the most important resources used in today's world is image. Medical images can play an essential role in helping diagnose diseases. Doctors and specialists use medical images to diagnose brain diseases. Convolution neural networks are among the most important deep learning methods in which several layers are trained powerfully. The proposed method is a deep parallel convolution neural network model consisting of AlexNet and VGGNet networks. The structure of the layers is different for the two types of networks. The features are combined at one point and then categorized using the softmax function. To test the network, we used different criteria with images in the database. In clinical diagnosis, generalizability means prediction for people from whom we have no observation. On this account, we did not include individuals' observations in training set in the test set. Compared to the existing models, the proposed model results show that our network has achieved better results. The best result for the database was FIGSHARE, which achieved 99.14% accuracy on binary class and 98.78% on multi-class, which was much better than other SVM models. We tested the best available result with the existing database and compared the results with the proposed model, which was the best model answer for all evaluation criteria. These results suggest that the proposed model can be used as an effective decision support tool for radiologists in medical diagnosis.