PCOS Image Detection Using MoblileNetV2 Transfer Learning on Ovarian Ultrasound Images

Abstract

Polycystic Ovary Syndrome (PCOS) is one of the most common hormonal disorders affecting women of reproductive age. Early diagnosis is essential to reduce long-term health complications and improve treatment outcomes. However, manual interpretation of ovarian ultrasound images is time-consuming and depends on the expertise of medical professionals. This paper presents an automated PCOS image detection system using the MoblileNetV2 deep learning architecture with transfer learning. Ovarian ultrasound images are preprocessed through resizing, normalization, and data augmentation before being used for model training. MoblileNetV2 automatically extracts discriminative image features and classifies the images into two categories: Infected (PCOS Positive) and Not Infected (PCOS Negative). The model is evaluated using Accuracy, Precision, Recall, F1-Score, and Area Under the ROC Curve (AUC). Experimental results demonstrate excellent classification performance, achieving 100.00% test accuracy, Precision = 1.00, Recall = 1.00, F1-Score = 1.00, and AUC = 1.0000 on the prepared dataset. A Streamlit-based web application is also developed to provide real-time prediction through a simple graphical interface. The proposed system demonstrates the effectiveness of transfer learning for automated PCOS detection and has potential applications in computer-aided medical diagnosis.

Introduction

The paper presents an AI-based Polycystic Ovary Syndrome (PCOS) detection system using a MobileNetV2 transfer learning model for automated classification of ovarian ultrasound images. PCOS is a common hormonal disorder affecting women of reproductive age and can cause irregular menstruation, infertility, and metabolic complications. Early diagnosis is important, but traditional ultrasound analysis is often manual, time-consuming, and dependent on the expertise of healthcare professionals.

The proposed system uses deep learning and transfer learning techniques to automate PCOS detection. MobileNetV2 is selected because of its lightweight architecture, high accuracy, faster training speed, and lower computational requirements compared with traditional deep learning models. The model classifies ultrasound images into two categories: PCOS Positive (Infected) and PCOS Negative (Not Infected). A Streamlit-based web application is also developed to allow users to upload ultrasound images and receive real-time predictions.

The literature review highlights the effectiveness of Convolutional Neural Networks (CNNs) and transfer learning models such as VGG16, ResNet, InceptionV3, DenseNet, EfficientNet, and MobileNet for medical image analysis. Traditional PCOS diagnosis methods rely on ultrasound examination, hormonal analysis, and clinical assessment, but manual interpretation can introduce variability. Transfer learning reduces the need for large datasets and high computational resources by utilizing knowledge from pretrained models.

Proposed Methodology

The system follows a structured workflow:

Dataset Collection → Image Preprocessing → MobileNetV2 Transfer Learning → Model Training → Performance Evaluation → Streamlit Deployment → PCOS Prediction

1. Dataset Collection
   • The dataset contains ovarian ultrasound images divided into two classes:
     • PCOS Positive (Infected)
     • PCOS Negative (Not Infected)
   • Images are organized into training and testing datasets for model development and evaluation.
2. Image Preprocessing
   • Images are resized to 224 × 224 pixels.
   • Pixel values are normalized.
   • Data augmentation techniques are applied to improve model generalization.
   • Images are converted into batches for efficient training.
3. MobileNetV2 Transfer Learning Model
   • A pretrained MobileNetV2 model with ImageNet weights is used as the feature extractor.
   • The original classification layer is replaced with a binary classification layer.
   • The model is trained using:
     • Adam optimizer
     • Binary Cross-Entropy loss function
     • 25 training epochs
4. Model Evaluation
   • The trained model is evaluated using:
     • Accuracy
     • Precision
     • Recall
     • F1-score
     • AUC score
5. Streamlit Deployment
   • The trained model is integrated into a web application.
   • Users can upload ultrasound images and receive:
     • Predicted PCOS class
     • Prediction confidence score

Experimental Results

The system was implemented using Python, TensorFlow, Keras, and Streamlit. The MobileNetV2 model was trained and tested on ultrasound image data.

The model achieved excellent performance:

The classification report also showed perfect performance for both categories:

• PCOS Positive: Precision = 1.00, Recall = 1.00, F1-score = 1.00
• PCOS Negative: Precision = 1.00, Recall = 1.00, F1-score = 1.00

The results indicate that MobileNetV2 effectively extracts important image features and accurately distinguishes between PCOS-positive and PCOS-negative ultrasound images. The lightweight architecture improves training efficiency while maintaining high classification performance.

References

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Copyright

Copyright © 2026 Deekshitha Nakkana, Gangadhar Doma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.