Paper Title
Improve Medical Image Diagnosis in Healthcare Utilizing A Framework for The Web Application

Abstract
In today's fast-paced world, the demand for accurate and efficient medical diagnosis has become increasingly essential in the field of healthcare. The core concept driving clinical diagnosis is to minimize human error in medical settings, a principle that extends beyond healthcare to other domains such as earth observation via satellites and comprehending activities in outer space. The primary motivation behind the development of our project lies in providing doctors with a reliable tool to predict and address potential health issues through a user-friendly web platform. Additionally, we aim to enhance the overall user experience by implementing features that manage user history and preferences. The technologies at the heart of our solution involve cutting-edge convolutional neural networks (CNN) and the powerful EfficientNet B3 for image processing, combined with the versatility of React.js for crafting an interactive web front-end. Our project is firmly grounded in image data, addressing common challenges in image processing, including overfitting, hyperparameter sensitivity, and time consumption. By tackling these issues head-on, we aim to empower medical professionals with rapid and accurate diagnostic results, aligning with their need for swift decision-making and optimal patient care.

Author - P.Shanmugam, Keshav S.R, Mithilesh Kumaar J.S

Published : Volume-11,Issue-1  ( Jan, 2024 )

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