Content-Based Image Retrieval and Image Classification System for Early Prediction of Bladder Cancer

• Published in: Diagnostics (Basel) 2024-12, Vol.14 (23), p.2637
• Main Author: Yildirim, Muhammed
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bladder cancer; Cancer therapies; Classification; Datasets; Deep learning; Diagnosis; diagnosis of disease; Disease; feature extraction; Health aspects; Image interpretation, Computer assisted; Image processing; Image retrieval; retrieval; Technology application
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14232637

Bladder cancer is a type of cancer that begins in the cells lining the inner surface of the bladder. Although it usually begins in the bladder, it can spread to surrounding tissues, lymph nodes, and other organs in later stages. Early detection of bladder cancer is, therefore, of great importance. Therefore, this study developed two systems based on classification and Content-Based Image Retrieval (CBIR). The primary purpose of CBIR systems is to compare the visual similarities of a user-provided image with the images in the database and return the most similar ones. CBIR systems offer an effective search and retrieval mechanism by directly using the content of the image data. In the proposed CBIR system, five different CNNs, two different textural-based feature extraction methods, and seven different similarity measurement metrics were tested for feature selection and similarity measurement. Successful feature extraction methods and similarity measurement metrics formed the infrastructure of the developed system. Densenet201 was preferred for feature extraction in the developed system. The cosine metric was used in the proposed CBIR system as a similarity measurement metric, the most successful among seven different metrics. As a result, it was seen that the proposed CBIR model showed the highest success using the Densenet201 model for feature extraction and the Cosine similarity measurement method.