Advanced Liver Tumour Detection Using Optimized YOLOv8 Modules DOI Open Access
M. Samykano,

D. Venkatasekhar,

G. Shanmugasundaram

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 4, 2025

Health fraternity is invariably challenged with early diagnosis, detection, identification, classification, treatment and convalescence of globally prevalent life-threatening fatal diseases as liver cancer. The detection cancer through medical image processing technique so challenging that an iota deviation conspicuous among healthy tissues, benign tumour malignant tissues a matter wake up call. This work entailed introduction novel, optimized YOLOv8-based model for harnessing the strengths transformer-based feature extraction, global attention mechanisms, advanced aggregation techniques. was subjected to rigorous performance relevant methods messages parameters time again repeated refinements. Eventually, it concluded proposed surpasses all models in extant now terms precision, recall, means average precision (mAP). ascertained by inference drawn from model’s achievement attaining 95.34% 96.49% 97.31% [email protected]. In regard excels differentiating normal cases, tumours, tumours. These innovations represent significant step toward improving accuracy automated diagnosis systems, potential revolutionize clinical workflows enhance patient outcomes.

Language: Английский

Advanced Liver Tumour Detection Using Optimized YOLOv8 Modules DOI Open Access
M. Samykano,

D. Venkatasekhar,

G. Shanmugasundaram

et al.

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(2)

Published: April 4, 2025

Health fraternity is invariably challenged with early diagnosis, detection, identification, classification, treatment and convalescence of globally prevalent life-threatening fatal diseases as liver cancer. The detection cancer through medical image processing technique so challenging that an iota deviation conspicuous among healthy tissues, benign tumour malignant tissues a matter wake up call. This work entailed introduction novel, optimized YOLOv8-based model for harnessing the strengths transformer-based feature extraction, global attention mechanisms, advanced aggregation techniques. was subjected to rigorous performance relevant methods messages parameters time again repeated refinements. Eventually, it concluded proposed surpasses all models in extant now terms precision, recall, means average precision (mAP). ascertained by inference drawn from model’s achievement attaining 95.34% 96.49% 97.31% [email protected]. In regard excels differentiating normal cases, tumours, tumours. These innovations represent significant step toward improving accuracy automated diagnosis systems, potential revolutionize clinical workflows enhance patient outcomes.

Language: Английский

Citations

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