Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging

Atharva Divekar, Atharva Sonawane
12/30/2024
eess.IVcs.AIcs.CV

No official code found. Best implementation: ATHdevs/Auto-PCOS (1 star, last commit 2024-12-30).

Abstract

The AUTO-PCOS Classification Challenge seeks to advance the diagnostic capabilities of artificial intelligence (AI) in identifying Polycystic Ovary Syndrome (PCOS) through automated classification of healthy and unhealthy ultrasound frames. This report outlines our methodology for building a robust AI pipeline utilizing transfer learning with the InceptionV3 architecture to achieve high accuracy in binary classification. Preprocessing steps ensured the dataset was optimized for training, validation, and testing, while interpretability methods like LIME and saliency maps provided valuable insights into the model's decision-making. Our approach achieved an accuracy of 90.52%, with precision, recall, and F1-score metrics exceeding 90% on validation data, demonstrating its efficacy. The project underscores the transformative potential of AI in healthcare, particularly in addressing diagnostic challenges like PCOS. Key findings, challenges, and recommendations for future enhancements are discussed, highlighting the pathway for creating reliable, interpretable, and scalable AI-driven medical diagnostic tools.

View on arXivView PDF

Code Implementations(1)

ATHdevs/Auto-PCOSCommunity100%

Solution to the classification of PCOS Syndrome using pretrained imagnet models for AutoPCOS hackathon by MisaHUB

11Python, Jupyter NotebookFeb 29, 20241 years ago
imagnetoptimizationpretrained-models

Similar Papers

From Pretraining to Privacy: Federated Ultrasound Foundation Model with Self-Supervised Learning

85% match1 repo192024

A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications

85% match2025

Lightweight Classifier for Detecting Intracranial Hemorrhage in Ultrasound Data

84% match2025

Ultrasound Report Generation with Cross-Modality Feature Alignment via Unsupervised Guidance

83% match1 repo582024

AI-Powered Early Detection of Critical Diseases using Image Processing and Audio Analysis

83% match2025

Reliable Multi-View Learning with Conformal Prediction for Aortic Stenosis Classification in Echocardiography

83% match1 repo42024

Cite this paper

@article{divekar2024leveraging,
  title  = {Leveraging AI for Automatic Classification of PCOS Using Ultrasound Imaging},
  author = {Atharva Divekar and Atharva Sonawane},
  year   = {2024},
  eprint = {2501.01984},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2501.01984v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2501.01984.svg)](https://codeofpaper.com/paper/2501.01984)

Discussion