Technology Category
Computer Vision: Interpretability, Explainability, and TransparencyMachine Learning: Multimodal LearningPhilosophy and Ethics of AI: Accountability, Interpretability & Explainability
Smart Diagnosis and Early Intervention in PCOS: A Deep Learning Approach to Women's Reproductive Health
🤖 AI Summary
This study addresses the clinical challenge of early diagnosis of polycystic ovary syndrome (PCOS)—a prevalent endocrine disorder among women of reproductive age that is often associated with infertility and metabolic complications. To enhance diagnostic accuracy and reliability, the authors propose a deep learning framework based on transfer learning, integrating DenseNet201 and ResNet50 architectures, and augmented with MixUp and CutMix data augmentation techniques to significantly improve model generalization. Furthermore, interpretability is strengthened through the incorporation of explainable AI (XAI) methods, including SHAP, Grad-CAM, and LIME, thereby increasing diagnostic transparency. Evaluated on a dataset of 3,856 ovarian ultrasound images, the model achieves a validation accuracy of 99.80% and a low validation loss of 0.617, demonstrating high precision and trustworthiness in PCOS detection and advancing the reliable integration of AI into gynecological clinical practice.
Technology Category
Application Category
📝 Abstract
Polycystic Ovary Syndrome (PCOS) is a widespread disorder in women of reproductive age, characterized by a hormonal imbalance, irregular periods, and multiple ovarian cysts. Infertility, metabolic syndrome, and cardiovascular risks are long-term complications that make early detection essential. In this paper, we design a powerful framework based on transfer learning utilizing DenseNet201 and ResNet50 for classifying ovarian ultrasound images. The model was trained on an online dataset containing 3856 ultrasound images of cyst-infected and non-infected patients. Each ultrasound frame was resized to 224x224 pixels and encoded with precise pathological indicators. The MixUp and CutMix augmentation strategies were used to improve generalization, yielding a peak validation accuracy of 99.80% by Densenet201 and a validation loss of 0.617 with alpha values of 0.25 and 0.4, respectively. We evaluated the model's interpretability using leading Explainable AI (XAI) approaches such as SHAP, Grad-CAM, and LIME, reasoning with and presenting explicit visual reasons for the model's behaviors, therefore increasing the model's transparency. This study proposes an automated system for medical picture diagnosis that may be used effectively and confidently in clinical practice.
Problem
Research questions and friction points this paper is trying to address.
Polycystic Ovary Syndrome
early diagnosis
reproductive health
ovarian ultrasound
infertility
Innovation
Methods, ideas, or system contributions that make the work stand out.
transfer learning
data augmentation
explainable AI
ultrasound image classification
PCOS diagnosis
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