Explainable AI for Medical Image Analysis in Medical Cyber-Physical Systems: Enhancing Transparency and Trustworthiness of IoMT.

Medical image analysis plays a crucial role in healthcare systems of Internet of Medical Things (IoMT), aiding in the diagnosis, treatment planning, and monitoring of various diseases. With the increasing adoption of artificial intelligence (AI) techniques in medical image analysis, there is a growing need for transparency and trustworthiness in decision-making. This study explores the application of explainable AI (XAI) in the context of medical image analysis within medical cyber-physical systems (MCPS) to enhance transparency and trustworthiness. To this end, this study proposes an explainable framework that integrates machine learning and knowledge reasoning. The explainability of the model is realized when the framework evolution target feature results and reasoning results are the same and are relatively reliable. However, using these technologies also presents new challenges, including the need to ensure the security and privacy of patient data from IoMT. Therefore, attack detection is an essential aspect of MCPS security. For the MCPS model with only sensor attacks, the necessary and sufficient conditions for detecting attacks are given based on the definition of sparse observability. The corresponding attack detector and state estimator are designed by assuming that some IoMT sensors are under protection. It is expounded that the IoMT sensors under protection play an important role in improving the efficiency of attack detection and state estimation. The experimental results show that the XAI in the context of medical image analysis within MCPS improves the accuracy of lesion classification, effectively removes low-quality medical images, and realizes the explainability of recognition results. This helps doctors understand the logic of the system's decision-making and can choose whether to trust the results based on the explanation given by the framework.

Effective CBMIR System Using Hybrid Features-Based Independent Condensed Nearest Neighbor Model.

In recent times, a large number of medical images are generated, due to the evolution of digital imaging modalities and computer vision application. Due to variation in the shape and size of the images, the retrieval task becomes more tedious in the large medical databases. So, it is essential in designing an effective automated system for medical image retrieval. In this research study, the input medical images are acquired from new Pap smear dataset, and then, the visible quality of acquired medical images is improved by applying image normalization technique. Furthermore, the hybrid feature extraction is accomplished using histogram of oriented gradients and modified local binary pattern to extract the color and texture feature vectors that significantly reduces the semantic gap between the feature vectors. The obtained feature vectors are fed to the independent condensed nearest neighbor classifier to classify the seven classes of cell images. Finally, relevant medical images are retrieved using chi square distance measure. Simulation results confirmed that the proposed model obtained effective performance in image retrieval in light of specificity, recall, precision, accuracy, and f-score. The proposed model almost achieved 98.88% of retrieval accuracy, which is better compared to other deep learning models such as long short-term memory network, deep neural network, and convolutional neural network.