Background: In the field of veterinary medicine, the accurate and timely diagnosis of progressive skin lesions in dogs, remains a critical challenge. Traditional diagnostic approaches are often limited by subjectivity and the need for extensive human expertise. This research utilized advanced Convolutional Neural Network (CNN) technology to enhance dermatological diagnostic accuracy by filling a gap in the field. 
 
Methods:&nbsp; A CNN model was trained to recognize patterns and variations in images of dog skin conditions including hot spots, rashes and sores. The model was trained to classify and distinguish between different progressive skin lesions in dogs with high accuracy, based on images from various internet sources. 
 
Result: The CNN model achieved an impressive 98.4% overall accuracy after training, demonstrating its potential in image classification for diagnosing progressive skin lesions in dogs. This deep-learning approach could significantly improve veterinary dermatology by providing a precise diagnostic tool for canine skin lesions.

Detection of Skin Lesions in Dogs using Advanced Convolutional&nbsp; Neural Network Technology

Background: Animal health and well-being&nbsp;depend on the early identification and treatment of diseases, especially skin conditions. Machine learning (ML) techniques, such as convolutional neural networks (CNNs), offer promising tools for automating disease detection based on visual symptoms. 
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Methods: This study employed a sequential CNN algorithm for skin lesion detection using digital images. The dog lesion dataset which consisted of 551 images categorized into five lesions: bumps, hair loss, hot spots, rashes and sores classes, was utilized for training and testing of the CNN model. The outcomes of the proposed model after training and testing were obtained in terms of performance metrics such as recall, accuracy, precision and F1-score. The adaptability and effectiveness of the model in classifying dog skin lesions were accessed by the overall accuracy of positive prediction. 
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Result: The developed model achieved a remarkable overall accuracy rate of 84.38% in classifying lesion classes. Its precision and recall tests validate its dependability, underscoring its potential to improve ailments detection and treatment in dogs, eventually increasing their well-being. 
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Detection of Skin Lesions in Dogs using Advanced Convolutional Neural Network Technology

Sequential Convolutional Neural Network Techniques to Improve Detection of Lesions in Dogs