Abstract:
This undergraduate project aims to enhance cancer detection accuracy in histopathological images through the development of an innovative algorithm named the Improved Water Strider Algorithm with Convolutional Autoencoder. Leveraging Python and web technologies, the system focuses on detecting lung and colon cancers with improved precision, utilizing deep learning techniques for image analysis.
Existing System:
Current cancer detection systems for histopathological images often suffer from limitations in sensitivity and specificity. Traditional methods rely on manual analysis, leading to subjective interpretations and potential diagnostic errors. The need for automated and accurate cancer detection algorithms is critical for improving early diagnosis and treatment.
Proposed System:
The proposed system introduces the Improved Water Strider Algorithm, integrating a Convolutional Autoencoder for feature extraction from histopathological images. This algorithm aims to enhance the sensitivity and specificity of cancer detection, providing more reliable results compared to existing methods. The system’s focus is on lung and colon cancers, two prevalent types with distinct histopathological characteristics.
System Requirements:
- Python programming language
- High-performance computing infrastructure
- Histopathological image datasets containing lung and colon cancer samples
- Libraries: TensorFlow, Keras, NumPy, OpenCV
Algorithms:
The Improved Water Strider Algorithm utilizes Convolutional Neural Networks (CNNs) for image classification, augmented by a Convolutional Autoencoder for feature extraction. Transfer learning techniques, employing pre-trained models such as VGG16 and custom-designed autoencoders, enhance the algorithm’s ability to discern subtle patterns indicative of cancerous regions.
Hardware and Software Requirements:
- Hardware: High-performance GPUs for accelerated deep learning computations
- Software: Python, TensorFlow, Keras, NumPy, OpenCV
Architecture:
The system architecture comprises image preprocessing modules, including resizing and normalization, followed by a multi-layered neural network integrating the Improved Water Strider Algorithm and Convolutional Autoencoder. The architecture is designed to handle the intricacies of histopathological images, capturing fine-grained details for accurate cancer detection.
Technologies Used:
- Deep Learning: CNNs, Transfer Learning, Convolutional Autoencoder
- Python: Core programming language
- TensorFlow, Keras: Deep learning frameworks
- NumPy, OpenCV: Image processing libraries
Web User Interface:
The system includes an intuitive web interface allowing users to upload histopathological images for analysis. The interface presents detailed reports highlighting identified cancerous regions, accompanied by visualizations and probability scores. Real-time feedback and interactive tools aid in the interpretation of results, facilitating seamless interaction for medical professionals.
In conclusion, this undergraduate project presents an innovative approach to lung and colon cancer detection in histopathological images. The Improved Water Strider Algorithm, coupled with Convolutional Autoencoder, offers a promising solution for enhancing sensitivity and specificity in cancer diagnosis, potentially contributing to improved patient outcomes and more effective medical decision-making.
UML DIAGRAM’S
Collaboration Diagram
Architecture diagram
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use case diagram
activity diagram
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Deployment Diagram
Flow Chart Diagram
