Web page Image Segmentation Using Neural Networks

Web page Image Segmentation Using Neural Networks

Prof.Hemalatha, Bathula Vamshi Krishna, Banda Vaishnavi, B.V.S.S.Surya Prakash, Bethi Thrilok reddy

Malla Reddy University
Hyderabad, Telangana

Abstract—

This study introduces a web-based image segmentation system utilizing Flask, a lightweight web framework, and ResNet (Residual Neural Network), a deep learning architecture renowned for image recognition tasks. The proposed framework seamlessly integrates Flask to create a user-friendly web application, providing an accessible interface for image segmentation. The backbone of the segmentation model is ResNet, renowned for its ability to capture intricate features in images, enhancing the accuracy of segmentation. Through the utilization of transfer learning, the ResNet model is fine-tuned on diverse datasets to adapt to various segmentation challenges. The web application allows users to upload images and receive real-time segmentation results, demonstrating the efficiency and practicality of the proposed solution. The integration of Flask and ResNet provides a versatile tool for researchers, developers, and practitioners interested in deploying robust image segmentation solutions within a web-based environment.

This research introduces an innovative approach to web-based image segmentation by leveraging Flask, a lightweight web framework, and ResNet (Residual Neural Network), a powerful deep learning architecture. Our system offers a user-friendly web application interface for image segmentation, making it accessible and practical for a broad user base. The segmentation model, based on ResNet, is chosen for its ability to capture intricate features in images, enhancing the precision of segmentation tasks. To adapt the model to diverse segmentation challenges, transfer learning is employed, allowing the network to leverage pre-trained knowledge on large datasets.

The integration of Flask provides a seamless and responsive web experience, allowing users to upload images and receive real-time segmentation results. This web-based approach enhances the versatility and accessibility of image segmentation tools, making them readily available to researchers, developers, and practitioners. The proposed system not only demonstrates robust performance in accurately segmenting objects but also showcases the efficiency of Flask in deploying machine learning models on the web.

The framework's applicability spans various domains, including medical image analysis, autonomous systems, and industrial automation. Through extensive experimentation and validation, our solution proves to be an effective and practical tool for users interested in deploying ResNet-based image segmentation within a web-based environment. The integration of Flask and ResNet contributes to the growing landscape of user-friendly and efficient solutions for image analysis tasks on the web.