Discovering Knee Osteoarthritis Using CNN enhanced With Alex Net

Project Code :TCMAPY1072


The objective of this study is to develop a deep learning model by enhancing Convolutional Neural Networks (CNN) with the Alex Net architecture for the accurate and early detection of knee osteoarthritis through medical image classification. This research aims to improve diagnostic efficiency, enabling timely intervention and better patient outcomes.


Knee Osteoarthritis (OA) is a prevalent and debilitating musculoskeletal condition that significantly impacts the quality of life for millions of individuals worldwide. Early and accurate detection is crucial for effective intervention and management. This study introduces an innovative approach employing a Convolutional Neural Network (CNN) enhanced with the renowned AlexNet architecture for the classification of Knee OA. Leveraging a dataset of medical images, including X-rays and MRI scans, our model harnesses the deep learning capabilities to extract intricate features and patterns. Preliminary results demonstrate exceptional classification accuracy, holding great potential to revolutionize Knee OA diagnosis, enabling timely and precise interventions, ultimately improving patient outcomes.

KEYWORDS: Osteoarthritis dataset, CNN, Alxenet etc.

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

Block Diagram



Processor - I3/Intel Processor

Hard Disk - 160GB

Key Board - Standard Windows Keyboard

Mouse - Two or Three Button Mouse

Monitor - SVGA



Operating System :  Windows 7/8/10

Server side Script :  HTML, CSS, Bootstrap & JS

Programming Language :  Python

Libraries :  Flask, Pandas, Mysql.connector, Os, Smtplib, Numpy

IDE/Workbench :  PyCharm

Technology :  Python 3.6+

Server Deployment :  Xampp Server

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