Dog Bite Detection and Hybrid Recovery Mechanism to Ensure Human Safety using Deep Learning

Abstract

Introduction: Dog bites are a serious public health issue that cause medical complications and require immediate interventions. Human decisions regarding the severity of the bites are subjective and unreliable, thereby leading to uneven treatment outcomes. An automatic dog bite detector and a novel approach to severity classification enhance the speed and efficiency of response. By pre-processing input data and executing machine learning methods, severity of the dog bites' wounds is classified into minor, moderate, and severe. Personalized medical treatments are facilitated by the system, enhancing patient care and public safety. Interfacing with veterinary clinics, healthcare centers, and emergency response teams ensures immediate assessment and treatment. Clinical effectiveness and workability will be tested in a pilot study to develop a more systematic and evidence-based approach towards handling dog bite cases.

Objectives: Detection and classification of severity of dog bites enhance efficacy and response rate. Machine learning categorization of the bite as minor, moderate, and severe enhances timely and proper medical intervention and helps enhance treatment result. Integration of the system in veterinary clinics and health centers is directed towards quick assessment and response. Clinical efficacy of the planned intervention will be assessed through pilot studies to aim at applying systematic and evidence-based methods in an attempt to provide effective treatment of dog bites and community safety.

Methods: Dog bite detection and severity classification employ a systematic process of preprocessing, feature extraction, and classification for precise estimation of dog bite severity. Preprocessing normalizes the datasets through resizing, pixel normalization, and data augmentation. Feature extraction employs CNN for wound features and ResNet-50 with residual connections for improved accuracy. Hybrid architecture CNN-ResNet50 classifies wounds, detects severity, and suggests treatments. Through integration, it facilitates feature learning, reduces diagnostic error, and increases speed of medical response, thereby facilitating timely and uniform treatment of dog bites.

Results: Dog Bite Diagnosis and Classification System gives real-time evaluation, accurate diagnosis, and sufficient treatment. It reduces human error, indexes emergency cases, and optimizes healthcare resources. It allows for remote AI-facilitated consultations, optimizes accessibility, especially in rural regions. It is available to all the population, offering protection, timely response, and medical reliability.

Conclusions: Dog Bite Detection and Classification System uses deep learning, the integration of CNN and ResNet-50, to effectively detect and classify dog bites in vet images. Through improved diagnostic precision, less work for veterinarians, and faster decision-making on the treatment, it guarantees effective medical intervention. AI-led innovation transforms veterinary diagnosis, establishing a new benchmark in animal care by improving accuracy, speed, and quality of treatment.