METHOD FOR DIAGNOSING SKIN DISEASES

Abstract

ABSTRACT A method (100) for diagnosing skin diseases. Further, the method comprising capturing a real-time image of a user's skin using an imaging device. Further, the method (100) comprising the steps of processing the captured image to detect and extract features indicative of various skin conditions through a trained machine learning model. Further, the method (100) comprising the steps of classifying the detected features into predefined categories of skin diseases based on learned patterns from a dataset of annotated skin images. Further, the method (100) comprising the steps of providing a diagnosis and recommended treatment options based on the classification results. Further, the method (100) comprising the steps of facilitating user interaction through a web-based interface that allows users to upload images and receive feedback regarding their skin condition.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of artificial intelligence in healthcare and, in particular, to a method for diagnosing skin diseases using an AI-enabled system that employs machine learning algorithms to analyze skin images, thereby improving diagnostic accuracy and facilitating timely medical intervention.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] The increasing prevalence of skin diseases, including conditions such as melanoma, psoriasis, and eczema, poses s , Claims:1. A method (100) for diagnosing skin diseases, the method comprising the steps of:
capturing a real-time image of a user's skin using an imaging device;
processing the captured image to detect and extract features indicative of various skin conditions through a trained machine learning model;
classifying the detected features into predefined categories of skin diseases based on learned patterns from a dataset of annotated skin images;
providing a diagnosis and recommended treatment options based on the classification results; and
facilitating user interaction through a web-based interface that allows users to upload images and receive feedback regarding their skin condition.

2. The method (100) as claimed in claim 1, the trained machine learning model employs convolutional neural networks (CNNs) and transfer learning techniques to enhance the accuracy of skin disease classification based on the extracted features.

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