MICRO ORGANISM IMAGE CLASSIFICATION USING DEEP LEARNING

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U PRATHIBHA; SNI SYED SADIQ

MICRO ORGANISM IMAGE CLASSIFICATION USING DEEP LEARNING

Authors: U PRATHIBHA, SNI SYED SADIQ

Unique Paper ID: 176660
Volume: 11
Issue: 11
PageNo: 7416-7420

Keywords: Machine Learning Deep Learning price prediction python

Abstract:
The Microorganism image classification has emerged as a crucial tool in fields such as medical diagnostics, environmental monitoring, and agricultural research. Traditional identification methods often require extensive time, expert knowledge, and laboratory resources. With advancements in computer vision and artificial intelligence, automated classification using microscopic images has gained traction, offering faster and more accurate alternatives. This book explores the development and application of deep learning, particularly Convolutional Neural Networks (CNNs), in classifying various microorganisms based on image data. Through an in-depth analysis of model architecture, training techniques, and evaluation metrics, this work demonstrates how AI can enhance microorganism classification and support critical decision-making processes in biological sciences. The content aims to guide researchers, students, and professionals in building efficient, scalable image classification systems tailored for microbiological use cases.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 11
PageNo: 7416-7420

MICRO ORGANISM IMAGE CLASSIFICATION USING DEEP LEARNING

Available:https://ijirt.org/article?manuscript=176660

20 Apr, 2025

Design and Implementation of a Decision Tree Algorithm-Based Network Intrusion Detection System

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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