Abstract
This paper addresses the problem of content-based image retrieval (CBIR) in a database of medical images using a two-step strategy. The first step consists in building a classification model using a state-of-the-art convolutional neural network for a preliminary screening of the query images. The classification model is trained using a weighted cross-entropy cost function that accounts for the similarity between classes. The second step of our CBIR method consists in searching for similar images in the database given the predicted class from the previous step. A histogram of oriented gradients (HOG) feature descriptor is used to reduce all images to lower dimensional feature vectors, and the similarity between a query image and the images in the database is evaluated by computing the Euclidean distance between the HOG feature vectors. The proposed method achieved an error score of 123.02 on the IRMA dataset, which represents an improvement of 7.12% over the state-of-the-art result.
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Acknowledgements
We acknowledge financial support from the German Academic Exchange Service (DAAD) and the University of Cape Town.
Computations were performed using facilities provided by the University of Cape Town’s ICTS High Performance Computing team: https://ucthpc.uct.ac.za/.
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Hounkanrin, A., Amayo, P., Nicolls, F. (2022). Content-Based Medical Image Retrieval Using a Class Similarity-Aware Cross-Entropy Loss. In: Pillay, A., Jembere, E., Gerber, A. (eds) Artificial Intelligence Research. SACAIR 2022. Communications in Computer and Information Science, vol 1734. Springer, Cham. https://doi.org/10.1007/978-3-031-22321-1_2
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