Early Classification of Bladder Cancer Using Spectrum-Aided Visual Enhancer (SAVE) and Deep Learning Models: A Non-Invasive Technology for Faster Detection.
Authors
Affiliations (9)
Affiliations (9)
- Institute of Medicine, Chung Shan Medical University, 402 No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan.
- Department of Urology, Chung Shan Medical University Hospital, 402 No. 110, Section 1, Jianguo North Road, Taichung 40201, Taiwan.
- Department of Mechanical Engineering, National Chung Cheng University, 168, University Rd., Minxiong Township, Chia Yi 62102, Taiwan.
- Department of Biomedical Engineering, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India.
- Department of Computer Science Engineering, School of Engineering and Technology, Sanjivani University, Kopargaon 423603, Maharashtra, India.
- Department of Integrated B.Tech, School of Engineering and Technology, Sanjivani University, Kopargaon 423603, Maharashtra, India.
- Department of Surgery, Urological Surgery Division, Kaohsiung Armed Forces General Hospital, 2, Zhongzheng 1st Rd., Lingya District, Kaohsiung 80284, Taiwan.
- Department of Computer Science and Engineering, Chitkara University, Chandigarh-Patiala National Highway NH-64 Village Jansla, Rajpura 140401, Punjab, India.
- Department of Technology Development, Hitspectra Intelligent Technology Co., Ltd., Kaohsiung 80661, Taiwan.
Abstract
Bladder cancer (BC) is a significant global health issue, ranking as the ninth most prevalent cancer with a rising incidence. Conventional diagnostic methods, including cystoscopy and standard imaging techniques, possess limitations in identifying early cancer symptoms and accurately staging bladder cancer. Consequently, this study developed a computer-aided diagnostic (CAD) system utilizing a novel, purely software-driven approach called Spectrum-Aided Vision Enhancer (SAVE) in conjunction with deep learning algorithms. Our results demonstrate the profound potential of SAVE to expand diagnostic accessibility in medical imaging. While achieving an overall performance comparable to standard WLI (overall <i>p</i> = 0.41, indicating strong non-inferiority), SAVE demonstrated targeted absolute improvements in F1-scores for the most challenging early stage categories without requiring expensive optical equipment. For instance, in the 'Above T1' class, SAVE elevated the F1-score from 65% to 85% utilizing VGG16. These findings indicate that SAVE can provide reliable baseline detection while enhancing visual cues for specific complex lesions without requiring expensive optical equipment. For instance, in the 'Above T1' class, SAVE elevated the F1-score from 65% to 85% utilizing VGG16. These findings indicate that SAVE can provide resource-constrained clinical settings with advanced, high-contrast diagnostic capabilities, effectively decentralizing precision urological care.