Detection Accuracy and False Negatives
Research gap analysis derived from 2 computer_science papers in our local library.
The gap
There is a need to improve detection accuracy and reduce false negatives in deep learning models for various applications such as fraud detection, breast cancer tumor detection, and smart kitchen hygiene monitoring.
Consensus across the literature
The papers collectively establish that current models have room for improvement in detection accuracy but leave open specific methods and metrics for achieving this improvement.
Research trend
Emerging — attention growing, methods still coalescing.
Supporting evidence — 2 representative gaps
- Deepfake Detection Based Smart Voting System (2026) · doi
The model achieved 81.7% accuracy but some fake images were not detected by the system (120 false negatives out of 500 fake samples), indicating room for improvement in detection sensitivity.
Keywords: fake model achieved accuracy images detected system false negatives samples indicating room improvement detection sensitivity - Real-Time Edge-Based Burglary Detection and Automated Alerting Using Deep Learning Framework (2026) · doi
The system achieved detection performance between 81-88%, indicating room for improvement in accuracy and the potential for false negatives in critical security scenarios.
Keywords: system achieved detection performance indicating room improvement accuracy potential false negatives critical security scenarios
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