Smith A, Carroll PW, Aravamuthan S, Walleser E, Lin H, Anklam K, Döpfer D, Apostolopoulos N. Computer vision model for the detection of canine pododermatitis and neoplasia of the paw.
Vet Dermatol 2024;
35:138-147. [PMID:
38057947 DOI:
10.1111/vde.13221]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND
Artificial intelligence (AI) has been used successfully in human dermatology. AI utilises convolutional neural networks (CNN) to accomplish tasks such as image classification, object detection and segmentation, facilitating early diagnosis. Computer vision (CV), a field of AI, has shown great results in detecting signs of human skin diseases. Canine paw skin diseases are a common problem in general veterinary practice, and computer vision tools could facilitate the detection and monitoring of disease processes. Currently, no such tool is available in veterinary dermatology.
ANIMALS
Digital images of paws from healthy dogs and paws with pododermatitis or neoplasia were used.
OBJECTIVES
We tested the novel object detection model Pawgnosis, a Tiny YOLOv4 image analysis model deployed on a microcomputer with a camera for the rapid detection of canine pododermatitis and neoplasia.
MATERIALS AND METHODS
The prediction performance metrics used to evaluate the models included mean average precision (mAP), precision, recall, average precision (AP) for accuracy and frames per second (FPS) for speed.
RESULTS
A large dataset labelled by a single individual (Dataset A) used to train a Tiny YOLOv4 model provided the best results with a mean mAP of 0.95, precision of 0.86, recall of 0.93 and 20 FPS.
CONCLUSIONS AND CLINICAL RELEVANCE
This novel object detection model has the potential for application in the field of veterinary dermatology.
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