Description:[0024]. The invention provides a novel approach to diagnosing and managing canine skin diseases using non-invasive techniques and advanced technology. The system comprises a combination of hardware and software components designed to capture, process, analyze, and interpret high-resolution images of affected skin areas in dogs.
[0025]. Hardware Components the system utilizes commonly available smartphones equipped with high-resolution cameras. These smartphones serve as the primary imaging device for capturing detailed images of the dog's skin lesions or affected areas. The use of smartphones ensures accessibility and ease of use for pet owners and veterinary professionals alike.
[0026]. Image Capture and Pre-processing the system includes a dedicated mobile application installed on the smartphone, which facilitates the capture of high-quality images of the dog's skin lesions. The application provides guidance to the user for optimal positioning and framing of the affected areas to ensure accurate imaging. Upon capturing the images, the system employs sophisticated pre-processing techniques to enhance the quality of the images. This pre-processing may include noise reduction, contrast enhancement, and color normalization to improve the clarity and detail of the captured skin lesions.
[0027]. Convolutional Neural Network (CNN) Analysis the pre-processed images are then fed into a CNN-based analysis module integrated within the mobile application. The CNN, based on the Inception Net architecture or similar deep learning models, is trained on a large dataset of canine skin disease images to recognize and classify various skin conditions accurately. The CNN analyzes the features present in the images, such as texture, color, and morphology, to identify specific patterns indicative of different skin diseases. The classification results generated by the CNN provide valuable insights into the nature and severity of the detected conditions.
[0028]. Expert System Integration the analysis results from the CNN are seamlessly integrated into an expert system embedded within the mobile application. The expert system comprises a rule-based reasoning engine and a comprehensive database of veterinary knowledge and diagnostic information. Using the classification results as input, the expert system applies logical rules and algorithms to interpret the findings and generate detailed diagnoses for the detected skin diseases. Additionally, the system cross-references the diagnosis with the stored database to provide relevant information on the recommended treatment options, prognosis, and additional diagnostic considerations.
[0029]. The system offers a user-friendly and efficient solution for diagnosing canine skin diseases, enabling pet owners and veterinary professionals to make informed decisions regarding the management and treatment of these conditions. By leveraging the power of smartphones, advanced image analysis techniques, and expert system integration, the invention aims to improve the accuracy, accessibility, and non-invasiveness of canine dermatological diagnosis and care.
[0030]. The presented invention offers a ground breaking solution to the longstanding challenges associated with diagnosing canine skin diseases. By harnessing the capabilities of smartphone technology, advanced image analysis techniques, and expert system integration, the system provides a non-invasive, efficient, and accurate method for identifying and managing various dermatological conditions in dogs.
[0031]. The utilization of high-resolution smartphone cameras enables pet owners and veterinary professionals to capture detailed images of affected skin areas conveniently and without the need for invasive procedures. Through sophisticated pre-processing techniques and Convolutional Neural Network (CNN) analysis, the system can accurately classify a wide range of canine skin diseases based on visual patterns and features extracted from the images.
[0032]. Moreover, the integration of an expert system enhances the diagnostic process by providing comprehensive interpretations of the analysis results and offering valuable insights into treatment options and prognoses. This integration ensures that users receive actionable information tailored to the specific needs of their canine companions.
[0033]. Overall, the invention represents a significant advancement in veterinary dermatology, offering not only improved diagnostic accuracy but also promoting the well-being of dogs by reducing stress associated with traditional diagnostic methods. Furthermore, by empowering pet owners with knowledge and facilitating informed decision-making, the system contributes to the broader goals of responsible pet ownership and compassionate pet care.
[0034]. In conclusion, the invention holds great promise for revolutionizing the field of canine dermatology, benefiting both individual pet owners and the veterinary community at large. Its impact extends beyond the realm of diagnosis, fostering a more knowledgeable and empathetic approach to caring for our furry companions.
[0035]. The invention titled "Diagnosis of Canine Skin Disease Using Non-Invasive Techniques" introduces a groundbreaking solution to address the pressing need for efficient, non-invasive, and accurate diagnosis of canine skin diseases. This innovative mobile and web application harnesses the power of smartphone cameras to capture high-resolution images of dogs' affected skin areas, employing sophisticated preprocessing techniques to enhance image quality. These images are then analyzed using Convolutional Neural Networks (CNNs), specifically the Inception Net architecture, ensuring precise classification of skin conditions. Moreover, the app's outputs are seamlessly integrated into an expert system that merges rule-based reasoning with a comprehensive database of information. This integration enables the system to provide thorough diagnoses and recommend appropriate remedies. By replacing invasive and stressful traditional diagnostic methods, this solution not only enhances the comfort of affected dogs but also addresses the challenges faced by caregivers. The impact of this invention extends beyond individual pet owners and their furry companions. It also benefits veterinary professionals and the broader landscape of pet healthcare. Additionally, the app serves as an educational tool, promoting responsible pet ownership by empowering users with knowledge about their pets' conditions. Informed decision-making not only improves the overall well-being of dogs but also fosters a more compassionate and knowledgeable pet care community. , Claims:1.A method for diagnosing canine skin diseases comprising:
a) Capturing high-resolution images of affected skin areas of dogs using smartphone cameras;
b) Pre-processing the captured images to enhance image quality;
c) Analysing the pre-processed images using Convolutional Neural Networks (CNNs) based on the Inception Net architecture to classify skin conditions; and
d) Integrating the analysis results into an expert system comprising rule-based reasoning and a database of information to provide diagnoses and recommend remedies.
2.The method as claimed in claim 1, wherein the pre-processing of the captured images comprises noise reduction, contrast enhancement, and color normalization.
3.The method as claimed in claim 1, wherein the CNNs are trained on a dataset comprising images of various canine skin diseases.
4.The method as claimed in claim 1, wherein the expert system further comprises a user interface accessible via a mobile application or a web application.
5.A system for diagnosing canine skin diseases comprising:
a) A smartphone camera for capturing high-resolution images of affected skin areas of dogs;
b) A pre-processing module configured to enhance image quality of the captured images;
c) A Convolutional Neural Network (CNN) based on the Inception Net architecture for analyzing the pre-processed images to classify skin conditions; and
d) An expert system comprising rule-based reasoning and a database of information, wherein the expert system integrates the analysis results to provide diagnoses and recommend remedies.
6.The system as claimed in claim 5, further comprising a user interface accessible via a mobile application or a web application, wherein the expert system outputs diagnoses and recommended remedies to the user interface.