ANALYSIS OF MICROSCOPIC IMAGES OF CELLS AND TISSUES USING AI, ML AND DL
Objective
The objective of this project is to develop an AI-driven system for the automated detection and classification of various cell types and tissue abnormalities in microscopic images. Using the YOLOv9 architecture for instance segmentation, the system aims to accurately identify and differentiate between histiocyte, lymphoma, mast cell, monocyte, neutrophil, normal cells, and sarcoma. The goal is to enhance diagnostic accuracy, reduce analysis time, and support healthcare professionals in early disease detection. By integrating Artificial Intelligence, Machine Learning, and Deep Learning techniques, the project strives to advance computational pathology and improve clinical decision-making in medical imaging.
Abstract
The analysis of microscopic images of cells and
tissues plays a critical role in diagnosing various diseases, including cancer
and autoimmune disorders. This project focuses on leveraging Artificial
Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques for
the automated detection and classification of different cell types and tissue
abnormalities, such as histiocyte, lymphoma, mast cell, monocyte, neutrophil,
normal cells, and sarcoma, using microscopic images. The dataset for this analysis
is sourced from the Roboflow Universe, specifically designed for YOLOv11-based
instance segmentation, enabling precise identification of distinct cell types.
Keywords: AI, Machine Learning, Deep Learning, YOLOv9, Microscopic Images, Cell Detection, Tissue Analysis, Instance Segmentation, Histiocyte, Lymphoma, Mast Cell, Monocyte, Neutrophil, Sarcoma, Medical Imaging, Computational Pathology.
NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.
Block Diagram
Specifications
SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS, Bootstrap & JS
Programming Language : Python
Libraries Flask, Pandas, Torch, Sklearn, Librosa, Numpy , Seaborn, Matplotlib
IDE/Workbench : VSCode
Server Deployment : Xampp Server
Database : MySQL
HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
RAM - 8GB (min)
Hard Disk - 128 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - Any
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Disclaimer - Takeoff Edu Group Projects are not associated or affiliated with IEEE in any way. The IEEE Projects mentioned here are mentioned in the context of student projects, whose ideas are derived from IEEE publications, not projects of or by IEEE.
