The advancement in biomedical engineering have been significant to the medical or healthcare industry. However, it faces issues like how it can be applied to the medicine and biology for healthcare aspects. Recently, quick advances of programming and equipment innovation have made simple, the issue of keeping up beneficial images accumulations. Visual elements like shading, and shape and composition are actualized for image retrieval. Conventional strategies for image indexing have been demonstrated neither reasonable nor effective regarding space and time so it set off the advancement of the new approach. A new concept called Content-based image retrieval (CBIR) is beneficial for the different sort of medical images having dissimilar imaging modalities, anatomic areas with diverse directions and biological schemes is projected. Classification of the medical image retrieval is the major concern for group of medical image. Hence, support vector machine (SVM) classifier can be favorable for grouping forecast of query and database images based on similarity matching. It is very difficult to detect the features of the compared images effectively for all the different types of queries. Hence, the proposed SVM-MIR aims to classify and retrieval of biomedical images using SVM classifier method. The SVM-MIR based classification considers numerous groups of medical images for analysis. The outcomes of the proposed SVM-MIR approach achieve better performance compared to the existing approach.

Most of the traditional approaches for medical image storage are least capable and scanning of relevant matching images are quite difficult. The existing approaches of content-based image retrieval (C-BIR) are less focused with medical images. The available research works with fuzzy logic approaches are very less and not efficient for medical image retrieval. Thus, there is a need of research work that can address both supervised and unsupervised learning approaches for medical image retrieval. Hence, the C-BIR technique is evolved with overcoming above stated concerns. Hence, this manuscript introduces two different C-BIR techniques using a support vector machine (SVM) and a fuzzy logic-based approach for classification. These approaches work on the classification based on feature extraction, region of Interest (ROI), corner detection, and similarity matching. The proposed approach has been analyzed for image retrieval for accuracy. The outcomes of the proposed study enhance the classification performances with retrieval than existing techniques of C-BIR.

No abstract

One of the important concepts in information & data analytics is the content-based image retrieval process. We are living in the information age. In the modern-day digital information technology imaging world, this is playing a predominant role in different sectors ranging from defense to research fields. Content-based image retrieval, also known as query by image content is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images or textual matters in large databases. The usage of digital images has been increased enormously from the last decade due to the drastic growth in storage & network technology. These technological changes have led professional users to use, store and manipulate remotely stored images. Information Retrieval (IR) deals with the location and retrieval of related documents or images based on user inputs such as keywords or examples as a query from the repository. This has motivated us to take up the research work on the CBIR concepts. Hence, to throw light into this chosen research topic, we are carrying out extensive research on the image feature synthesis and matching in content-based image retrieval systems using the concepts of AI, ML & Fuzzy Logic schemes, which could be used to improve the retrieval system's performance in CBIRs. A brief survey, i.e., an insight into the chosen research area in the field of content-based image retrievals was made & the same is being presented w.r.t. the work done by various researchers across the globe in the form of an extensive literature review. The work done by them was studied, lacunas observed & the problem was defined with a couple of good objectives to be solved four objectives were proposed as O1 - Investigation of the effectiveness of evolutionary computation in generating composite operator vectors for image, so that feature dimensionality is reduced to improve retrieval performances; O2 - Construction of the image-leve