User Interaction: Users begin by uploading an image through our intuitive interface.
Preprocessing: Once uploaded, the image undergoes preprocessing to ensure it is in the optimal format for analysis. This may include resizing, normalization, and enhancement to improve accuracy.
CNN Overview: We use Convolutional Neural Networks (CNNs), a type of deep learning algorithm highly effective for image recognition tasks. CNNs work by applying various filters to the image, detecting features such as edges, textures, and shapes.
Feature Extraction: The CNN processes the image through multiple layers, extracting hierarchical features from basic to complex. These features are crucial for identifying patterns and objects within the image.
Classification: After feature extraction, the CNN classifies the image based on learned patterns and pre-trained models. This step involves comparing the image features against a vast database of known images to determine possible categories or labels.
Generating Descriptions: Once the CNN has classified the image, the results are sent to a Large Language Model (LLM). The LLM is designed to understand and generate human-like text based on the data it receives.
Contextual Analysis: The LLM analyzes the classification results and generates detailed descriptions and interpretations. This may include explanations of the detected objects, possible diagnoses, or relevant information about the image content.
User-Friendly Output: The LLM presents the interpreted information in a clear and concise manner, making it accessible and understandable for users. This ensures that even complex or technical results are communicated effectively.
Display: The interpreted results are then displayed on the website, providing users with comprehensive insights into the image they uploaded. The interface is designed to be user-friendly, allowing easy navigation and understanding of the information.
User Feedback: We encourage users to provide feedback on the accuracy and relevance of the results. This feedback is invaluable for continuously improving the performance and reliability of our algorithms.
Ongoing Updates: Our system is regularly updated to incorporate advancements in CNN and LLM technologies, ensuring that we offer the most accurate and up-to-date analysis.