Published Paper Details:

Ovarian cysts are among the most common gynecological disorders affecting women across various age groups and may lead to serious health complications if not detected and classified at an early stage. Accurate identification of cyst types such as simple, complex, and polycystic ovarian conditions plays a critical role in determining appropriate treatment strategies and improving patient outcomes. However, conventional diagnostic approaches primarily depend on manual interpretation of ultrasound images by radiologists, which can be time-consuming, subjective, and prone to inter-observer variability. To address these challenges, this research proposes OvaSense, an intelligent deep learning-based diagnostic framework designed for automated detection and classification of ovarian cysts using ultrasound imaging. Furthermore, the OvaSense system is designed with scalability and usability in mind, allowing integration into clinical workflows and assisting healthcare professionals in early diagnosis and treatment planning. The results highlight the effectiveness of combining object detection and deep learning-based classification techniques for medical image analysis, making the proposed framework a promising decision-support tool for modern intelligent healthcare systems.