PCause is an innovative project aimed at revolutionizing the diagnosis of Polycystic Ovary Syndrome (PCOS) by integrating advanced technologies such as deep learning and image processing. The project's primary objective is to develop an automated PCOS detection system that enhances the accuracy and speed of diagnosis, thereby facilitating early intervention and improving healthcare outcomes for affected individuals.
The motivation behind PCause stems from the need to address the challenges associated with the current methods of PCOS diagnosis. Manual assessments and subjective criteria often lead to misdiagnosis or delayed intervention, highlighting the necessity for a standardized and efficient diagnostic approach. By leveraging deep learning and image processing technologies, PCause seeks to fill this gap by providing a reliable and automated solution for PCOS detection.
Polycystic Ovary Syndrome (PCOS) is a common hormonal disorder affecting women of reproductive age. Early detection and accurate diagnosis are crucial for effectively managing and preventing associated complications. However, existing diagnostic methods lack standardization and efficiency, posing challenges in providing timely and personalized healthcare for individuals with PCOS. PCause addresses this problem by developing an automated PCOS detection system that leverages advanced technologies to enhance early diagnosis and streamline treatment planning.
The primary goal of PCause is to correctly predict whether a patient is infected with PCOS or not using ultrasound images. By analyzing ultrasound images with deep learning models and image processing techniques, PCause aims to provide accurate and efficient diagnosis, contributing to improved healthcare outcomes for individuals affected by PCOS.
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Data Augmentation for Model Training: Implement data augmentation strategies to expand the training dataset. By exposing the model to a diverse set of images, this step enhances its generalization and performance.
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Feature Extraction from Image Dataset: Utilize image processing techniques to identify and extract relevant features from the image dataset. This step aims to enhance the model's accuracy and adaptability to variations in images.
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Conduct Comparative Analysis of Deep Learning Models: Perform a comprehensive comparative analysis of various deep learning models, including convolutional neural network architectures such as VGG-16, VGG-19, RESNET-50, and ALEXNET. Evaluate their performance based on the accuracy of the results they produce, providing insights into the effectiveness of different models under specific conditions.
To get started with PCause, follow these steps:
- Clone the repository:
git clone https://github.com/yourusername/PCause.git - Explore the provided notebooks for data augmentation, feature extraction, model training, and comparative analysis.
- Customize the project according to your requirements and datasets.