Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

The unavailability of an annotated dataset for a low-resource Ewe language makes it difficult to develop an automated system to appropriately evaluate public opinion on events, news, policies, and regulations. In this study, we collected and preprocessed a low-resourced document-level Ewe sentiment dataset based on social media comments. We used three features learned by word embeddings (Global vectors, word-to-vector, and FastText) rather than hand-crafted features. We further proposed a novel method termed MC2D-CNN+BiLSTM-Attn to detect the exact sentiment feature from the Ewe dataset. Extensive experiments indicate that the proposed method efficiently classifies various sentiments and is superior to benchmark deep learning methods. Results show that in detecting the precise sentiments from raw Ewe textual context, the BiLSTM incorporating Glove outperforms Word2Vec and FastText embedding with an accuracy of 0.727. Furthermore, Attn+BiLSTM and multichannel convolutional neural network methods incorporating the Word2Vec embedding layer perform better than Glove and FastText embedding with an accuracy of 0.848 and 0.896. In contrast, our proposed method with the same Word2Vec embedding recorded 0.949. Received: 8 August 2023 | Revised: 19 October 2023 | Accepted: 23 November 2023 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement Victor Kwaku Agbesi: Conceptualization, Methodology, Software, Formal analysis, Writing - original draft. Wenyu Chen: Supervision, Project administration. Chiagoziem C. Ukwuoma: Formal analysis, Writing - review & editing, Visualization, Project administration. Noble A. Kuadey: Investigation, Writing - review & editing, Visualization. Collinson Colin M. Agbesi: Investigation, Writing - review & editing. Chukwuebuka J. Ejiyi: Validation, Data curation. Emmanuel S. A. Gyarteng: Validation, Data curation. Gladys W. Muoka: Validation, Visualization. Anthony M. Kuadey: Software, Data curation.