CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis
<|reference_start|>CBIDR: A novel method for information retrieval combining image and data by means of TOPSIS applied to medical diagnosis: Content-Based Image Retrieval (CBIR) have shown promising results in the field of medical diagnosis, which aims to provide support to medical professionals (doctor or pathologist). However, the ultimate decision regarding the diagnosis is made by the medical professional, drawing upon their accumulated experience. In this context, we believe that artificial intelligence can play a pivotal role in addressing the challenges in medical diagnosis not by making the final decision but by assisting in the diagnosis process with the most relevant information. The CBIR methods use similarity metrics to compare feature vectors generated from images using Convolutional Neural Networks (CNNs). In addition to the information contained in medical images, clinical data about the patient is often available and is also relevant in the final decision-making process by medical professionals. In this paper, we propose a novel method named CBIDR, which leverage both medical images and clinical data of patient, combining them through the ranking algorithm TOPSIS. The goal is to aid medical professionals in their final diagnosis by retrieving images and clinical data of patient that are most similar to query data from the database. As a case study, we illustrate our CBIDR for diagnostic of oral cancer including histopathological images and clinical data of patient. Experimental results in terms of accuracy achieved 97.44% in Top-1 and 100% in Top-5 showing the effectiveness of the proposed approach.<|reference_end|>
@article{giuri2024cbidr:,
    title={CBIDR: A novel method for information retrieval combining image and data
  by means of TOPSIS applied to medical diagnosis},
    author={Humberto Giuri and Renato A. Krohling},
    journal={arXiv preprint arXiv:2410.06180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.06180},
    primaryClass={cs.IR cs.AI eess.IV}
}