A Survey of Machine Learning for Bladder Tumor Detection in Medical Imaging
DOI:
https://doi.org/10.29304/jqcsm.2026.18.22702Keywords:
Bladder Cancer deep learningAbstract
bladder cancer at an early stage and treating it successfully is extremely difficult due to its recurrence and spread. With the use of artificial intelligence and medical imaging tools to identify the disease, modern systems face many difficulties in applying them to different data sets; thus, they lack classification accuracy. Other problems with these models include a lack of spatial information and high computational complexity. On the other hand, although VIT models have shown promising results in medical image analysis, their performance still depends heavily on the quality of hyperparameter tuning, making them less efficient. This supports the early detection of bladder cancer, emphasizing the need to create a complete VIT-based system that is then enhanced in a meta-heuristic manner to increase detection accuracy while reducing computational complexity. In the Radiomics study, machine learning (ML) methods were systematically applied to identify sophisticated features and create analytical patterns for cancer classification and medical outcome prediction. Deep learning (DL) has significantly improved cancer detection, segmentation, and classification with convolutional neural networks and U-Net. Recently, vision transformer models have become well known for their ability to detect long-range connections in medical images. This proves that histological examination and magnetic resonance imaging allow for a new approach to bladder cancer treatment. Even if these different ideas have evolved, there are not enough routine tests to prove how well they work together. Based on bladder tumor detection techniques, This survey provides a systematic classification and diagnostic evaluation of current artificial intelligence, thereby emphasizing its methods, benefits, limitations, and potential directions for further investigation.
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