Deep Learning Applications in Medical Image Analysis

Project Code :TCMAPY1086


The objective of this project is to leverage Deep Learning techniques, specifically Convolutional Neural Networks (CNN) and MobileNet architecture, for the purpose of classifying medical images related to brain, liver, lung, and blood diseases. Through the implementation of these advanced algorithms, the project aims to achieve accurate and efficient classification, contributing to enhanced diagnostic capabilities in medical image analysis. The focus is on leveraging CNN and MobileNet to discern patterns and features within the images, facilitating automated disease classification for improved healthcare outcomes.


The unprecedented success of deep learning algorithms in image recognition coincides with a surge in the utilization of electronic medical records and diagnostic imaging. This review delves into the application of Deep learning algorithms in the realm of medical image analysis, with a particular focus on convolutional neural networks (CNNs), underscoring the clinical implications of these advancements. In an era characterized by the prolific generation of medical big data, Deep learning offers a distinct advantage, allowing for the automated discovery of intricate hierarchical relationships within the data without the need for painstaking manual feature engineering. This comprehensive exploration encompasses crucial research areas and applications in medical image analysis, spanning classification, localization, detection, segmentation, and registration, with a specific emphasis on diseases affecting the brain, liver, lungs, and blood. The discussion extends to the benefits of leveraging Deep learning in discerning patterns and features within diverse medical datasets, thereby enhancing diagnostic capabilities. The review concludes by addressing research challenges, highlighting emerging trends, and proposing potential future directions for the intersection of deep learning and medical image analysis. Overall, this synthesis provides a panoramic view of the evolving landscape where artificial intelligence augments clinical insights and transforms healthcare practices.

KEYWORDS: Convolutional Neural Networks (CNN’s), MobileNet.

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 7/8/10/11

Server side Script :  HTML, CSS, Bootstrap & JS

Programming Language :  Python

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

IDE/Workbench :  PyCharm or VS Code 

Technology :  Python 

Server Deployment :  Xampp Server

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