Medical Waste Classification using Deep Learning and Convolutional Neural Networks

Project Code :TCPGPY1834

Objective

The project develops an automated medical waste classification system using deep learning techniques, particularly CNNs. It classifies waste into 12 categories (e.g., syringes, plastic packaging) using models like MobileNet, EfficientNet, Swin Transformer, and Vision Transformer. The system, integrated with Flask, allows users to upload images for real-time waste classification.

Abstract

The project focuses on developing an automated system for medical waste classification using deep learning techniques, particularly convolutional neural networks (CNNs). The system classifies medical waste into 12 categories, such as body tissues, plastic packaging, and syringe needles, using advanced models like MobileNet, EfficientNet combined with GRU, Swin Transformer, and Vision Transformer. The backend of the system is powered by these models, which have been trained on a curated dataset of medical waste images. The frontend is implemented using HTML, CSS, and JavaScript, with Flask used for backend integration. After registering and logging in, users can upload images of medical waste to the platform, and the system predicts the category of the waste based on the uploaded image. This classification system enhances the efficiency of waste management by automating the identification and categorization process.

Keywords:

Medical Waste, Classification, Deep Learning, Convolutional Neural Networks, MobileNet, EfficientNet, GRU, Swin Transformer, Vision Transformer, Flask, Image Prediction, Waste Management.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.