Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases.

Monkeypox (MPox) is an infectious disease caused by the monkeypox virus, presenting challenges in accurate identification due to its resemblance to other diseases. This study introduces a deep learning-based method to distinguish visually similar diseases, specifically MPox, chickenpox, and measles, addressing the 2022 global MPox outbreak. A two-stage optimization approach was presented in the study. By analyzing pre-trained deep neural networks including 71 models, this study optimizes accuracy through transfer learning, fine-tuning, and ensemble learning techniques. ConvNeXtBase, Large, and XLarge models were identified achieving 97.5% accuracy in the first stage. Afterwards, some selection criteria were followed for the models identified in the first stage for use in ensemble learning technique within the optimization approach. The top-performing ensemble model, EM3 (composed of RegNetX160, ResNetRS101, and ResNet101), attains an AUC of 0.9971 in the second stage. Evaluation on unseen data ensures model robustness and enhances the study's overall validity and reliability. The design and implementation of the study have been optimized to address the limitations identified in the literature. This approach offers a rapid and highly accurate decision support system for timely MPox diagnosis, reducing human error, manual processes, and enhancing clinic efficiency. It aids in early MPox detection, addresses diverse disease challenges, and informs imaging device software development. The study's broad implications support global health efforts and showcase artificial intelligence potential in medical informatics for disease identification and diagnosis.

Cyber governance studies in ensuring cybersecurity: an overview of cybersecurity governance.

With the exponential increase of digital data in cyber environments, security measures have gained more importance. Cybersecurity threats are revealed by national and international units, and the number of these threats is increasing daily. The elimination of cybersecurity risks is possible with an effective cybersecurity strategy. Since the concept of management is not sufficient, the implementation of this strategy is possible with cyber governance, which includes all stakeholders in the management processes. This study emphasizes the importance and necessity of cyber governance in ensuring cybersecurity. The research and results for cybersecurity governance have been examined. A descriptive research model was used to this end. In terms of research philosophy, a basic research model and a documentary research model have been created with regard to the application method. The universe of the research consists of studies obtained from Web of Science, EBSCO, Scopus, Google Scholar, and TR Index. Studies from the last 5 years have been downloaded with the determined keywords. The result showed that although there are studies that produce local solutions for cybersecurity governance in different countries, a general governance framework has not been established as yet. On the contrary, there is a latent struggle to retain the management of this area, not its governance.

Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning.

Cerebrovascular accident due to carotid artery disease is the most common cause of death in developed countries following heart disease and cancer. For a reliable early detection of atherosclerosis, Intima Media Thickness (IMT) measurement and classification are important. A new method for decision support purpose for the classification of IMT was proposed in this study. Ultrasound images are used for IMT measurements. Images are classified and evaluated by experts. This is a manual procedure, so it causes subjectivity and variability in the IMT classification. Instead, this article proposes a methodology based on artificial intelligence methods for IMT classification. For this purpose, a deep learning strategy with multiple hidden layers has been developed. In order to create the proposed model, convolutional neural network algorithm, which is frequently used in image classification problems, is used. 501 ultrasound images from 153 patients were used to test the model. The images are classified by two specialists, then the model is trained and tested on the images, and the results are explained. The deep learning model in the study achieved an accuracy of 89.1% in the IMT classification with 89% sensitivity and 88% specificity. Thus, the assessments in this paper have shown that this methodology performs reasonable results for IMT classification.

Correction to: Classification of Carotid Artery Intima Media Thickness Ultrasound Images with Deep Learning.

The original article unfortunately contained a mistake. Figure 2b was removed in the article.