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

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. T...

Full description

Saved in:
Bibliographic Details
Published in:Diagnostics (Basel) 2024-11, Vol.14 (23), p.2637
Main Author: Yildirim, Muhammed
Format: Article
Language:English
Subjects:
Artificial intelligence
Bladder cancer
Cancer
Cancer therapies
Classification
Datasets
Deep learning
Diagnosis
diagnosis of disease
Disease
feature extraction
Health aspects
Image interpretation, Computer assisted
Image processing
Image retrieval
Oncology, Experimental
retrieval
Technology application
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2075-4418
2075-4418
DOI:10.3390/diagnostics14232637