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Introduction:

In recent years, image classification has become a significant research area due to the increasing availability of large datasets and the advancement of deep learning techniques. Convolutional Neural Networks (CNNs) have shown remarkable success in image classification tasks, outperforming traditional machine learning algorithms. Building an image classification system using CNNs involves training a neural network to recognize and classify images into predefined categories. This thesis focuses on developing and implementing an image classification system using CNNs to accurately classify images.

1.1 Introduction
1.2 Background of Study
1.3 Problem Statement
1.4 Objective of Study
1.5 Limitation of Study
1.6 Scope of Study
1.7 Significance of Study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter Two: Literature Review
2.1 Introduction to Convolutional Neural Networks
2.2 History of Image Classification
2.3 Deep Learning in Image Classification
2.4 Transfer Learning in Image Classification
2.5 Image Datasets for Training
2.6 Evaluation Metrics for Image Classification
2.7 Applications of Image Classification
2.8 Challenges in Image Classification
2.9 Current Trends in Image Classification
2.10 Summary of Literature Review

Chapter Three: System Design and Methodology
3.1 Introduction
3.2 Data Collection and Preprocessing
3.3 CNN Architecture Design
3.4 Training the Model
3.5 Hyperparameter Tuning
3.6 Data Augmentation Techniques
3.7 Model Evaluation
3.8 Performance Metrics
3.9 Comparison with Traditional Machine Learning Algorithms

Chapter Four: System Implementation
4.1 Introduction
4.2 Programming Environment
4.3 Building the Image Classification System
4.4 Model Deployment
4.5 Testing and Evaluation
4.6 Results and Analysis
4.7 Performance Optimization
4.8 System Maintenance
4.9 Challenges Faced in Implementation

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Achievements of the Study
5.3 Contribution to Knowledge
5.4 Implications for Future Research
5.5 Conclusion

Thesis Overview:

Image classification has always been a challenging task in the field of computer vision. The development and implementation of efficient image classification systems are crucial in various applications such as medical diagnosis, autonomous driving, and security surveillance. Convolutional Neural Networks (CNNs) have emerged as a powerful tool for image classification, with their ability to extract hierarchical features from raw pixel data.

This thesis aims to build an image classification system using CNNs and investigate the effectiveness of deep learning techniques in classifying images accurately. The study will begin with a comprehensive literature review on CNNs, image classification, transfer learning, and evaluation metrics. The system design and methodology chapter will cover data collection, preprocessing, CNN architecture design, model training, and evaluation.

The system implementation chapter will focus on the practical aspects of building the image classification system, including programming environment setup, model deployment, testing, and performance optimization. The thesis will conclude with a summary of findings, achievements of the study, contribution to knowledge, implications for future research, and a conclusion.

Overall, this thesis aims to contribute to the existing body of knowledge on image classification using CNNs and provide insights into the challenges and opportunities in building efficient image classification systems.

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