CelInsight:AI-Powered Web Solution for Cervical Cancer Classification
Rishabh Singh
School Of Computer Science and Engineering Presidency University
Bengaluru, Karnataka rishabh89003@gmail.com
Priya Jain
School Of Computer Science and Engineering Presidency University
Bengaluru, Karnataka 15priya12jain@gmail.com
Abstract—This paper introduces the system, a Python-based web platform developed for the secure management, processing, and intelligent analysis of medical images and video streams. The system leverages the great libraries and frameworks of Python, especially Flask, in building the backend.Through the emphasis on AI model integration of TensorFlow/PyTorch for better detection accuracy and precision.For the healthcare pro- fessionals.The report provides information about the architecture of the platform, the key features of the system, and how integrating AI transforms diagnostic workflows. Results indicate the system as a scalable, user-friendly, and secure tool for the management of medical images in health settings. It addresses the problem of cervical cancer as a health burden, with increased importance for low- and middle-income countries. Early and precise classification of cervical.Proper classification of cancer subtypes is crucial to guide the treatment decisions and to improve patient outcomes in those regions. In an attempt to solve this problem, the system develops a centralized imaging framework-based model intended for cervical cancer diagnosis classification. This framework is developed based on state-of-the- art imaging modalities and artificial intelligence. It improves the diagnostic workflow with greater efficiency and accuracy. The system presents hybrid models that are based on a combination of ideas of convolutional neural networks and Gradient-boosted machine learning classifiers are utilized for robust and reliable performance in the classification of various subtypes of cervical cancer. The system also ensures security and centralization of data; hence, the healthcare professionals get to work on scalable and accurate tools for the classification process. All these have been developed through Python-based technologies and incorporated into AI and secure infrastructure to make the system a pivotal innovation in modern diagnostics.
Index Terms—Medical image processing, AI in healthcare, cervical cancer diagnosis, deep learning, convolutional neural net- works (CNN), Gradient-boosted machine learning, TensorFlow, PyTorch, Flask, diagnostic workflow, secure medical imaging, intelligent analysis, centralized imaging framework, healthcare technology, AI-driven classification, medical video stream analy- sis.
