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https://github.com/harikishan-ai/pcos-detection-using-ultrasound-image

This project leverages convolutional neural networks (CNNs) to detect Polycystic Ovary Syndrome (PCOS) in ultrasound images by accurately identifying characteristic ovarian features. The goal is to provide a non-invasive, AI-powered tool for early and precise PCOS diagnosis.
https://github.com/harikishan-ai/pcos-detection-using-ultrasound-image

cnn-classification image-processing python3 tensorflow yolov8

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This project leverages convolutional neural networks (CNNs) to detect Polycystic Ovary Syndrome (PCOS) in ultrasound images by accurately identifying characteristic ovarian features. The goal is to provide a non-invasive, AI-powered tool for early and precise PCOS diagnosis.

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README 

        
# PCOS Detection Using Ultrasound Image



Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine condition in

reproductive-age women. Early and accurate diagnosis is crucial. This study

presents a novel approach, employing advanced picture segmentation and

CNN-based deep learning on a diverse PCOS ultrasound image dataset. We

preprocess images, segment ovarian follicles and cysts, and leverage deep

learning for automated feature extraction. Our method outperforms manual

interpretation, offering a more reliable and objective PCOS diagnosis. This

breakthrough has the potential to enhance patient outcomes and

reproductive health. Future research aims to refine and implement this

approach in clinical settings



## Problem Statement



The current diagnosis process heavily relies on subjective manual

interpretation of ultrasound images, which can be challenging and prone to

human bias. This study addresses this problem by proposing an innovative

ultrasound-based PCOS detection method that combines advanced

picture segmentation and CNN-based deep learning. The goal is to create

an automated and objective diagnostic solution that improves the

accuracy of PCOS identification and subsequently enhances patient

outcomes and reproductive health."



## Methodology



### 1. Data Collection

- **Source Diverse Data:** Gather a comprehensive set of ultrasound images from both PCOS and non-PCOS cases.

- **Data Quality:** Apply rigorous preprocessing steps to ensure data quality, uniformity, and consistency across the dataset.



### 2. Image Segmentation

- **Advanced Techniques:** Use cutting-edge segmentation techniques to accurately isolate relevant anatomical structures in the ultrasound images.

- **Focus Areas:** Concentrate on segmenting ovarian cysts and follicles, which are critical for PCOS diagnosis.



### 3. Deep Learning Model

- **CNN Architecture:** Develop a CNN-based model using YOLOv8.0.20, optimized for multi-class classification tasks.

- **Training:** Train the model on preprocessed and augmented data to improve generalization and robustness.



### 4. Validation and Testing

- **Cross-Validation:** Implement robust cross-validation techniques to assess the model's performance.

- **Independent Testing:** Evaluate the model on an independent test set, measuring accuracy, sensitivity, specificity, and other essential metrics.



## Results



![image](https://github.com/user-attachments/assets/7793f9e6-fa21-4b9f-b414-e8ad628cb0c3)



## Conclusion



Through the development and rigorous evaluation of our YOLOv8.0.20-based PCOS detection model, we have made significant strides in addressing the critical challenges associated with PCOS diagnosis. Our model has shown promising results in terms of accuracy, speed, and interpretability.

While the field of PCOS detection is evolving, our approach represents a significant step forward, benefiting from advanced image segmentation and deep learning techniques. Collaboration with medical experts and adherence to data security and privacy standards have also been integral to our success.

We acknowledge that the landscape of PCOS detection models is continuously evolving. As of our last update, we are proud to present YOLOv8.0.20 as a competitive solution. However, it's essential to remain open to further innovations and advancements in this field. Our commitment to continuous improvement ensures that we stay at the forefront of PCOS diagnosis using ultrasound images, providing accurate and reliable results for the benefit of patients and healthcare providers.