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.
Internet of Things (IoT) is one of the greatest advancements in technology especially in the medical field. The interconnection of medical devices with the internet makes it easier to identify problems and adapts with patient conditions. The sophisticated devices may either be worn or implanted in the users’ bodies to continually examine their wellbeing. But due to the availability of several sensors and communication systems, standardization has become a key issue. This survey paper presents the state-of-art research relating to the various sensors and communication models that are used to provide home based monitoring. The small sensor nodes with IoT and its influence on every patient’s life in reducing their anxiety of risk when they are inaccessible to medical support are studied. This study helps the researchers in choosing the best available protocols to implement in health-care devices. The contribution to the development of smart cities and data from home or at work for smart health care is discussed. The key findings of this study are the benefits of 5G technology for smart health care, as the most often utilized communication method in the literature to date is 4G. Also, the challenges faced in implementing the models in real time are discussed with the options of future scope mentioned.
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