ABSTRACT
Polycystic Ovary Syndrome (PCOS) using deep learning techniques like MobileNet and transfer learning for training purposes and the utilization of Keras preprocessing functionalities enables to use of the latest approaches and extract the spectrum of substantial features effectively. Transfer learning through the MobileNet has the potential to demonstrate prefabricated model capabilities for particular medical imaging tasks. Augmentation techniques such as zooming, shearing, and flipping enhance model robustness and generalization. The training process, spanning 30 epochs and carefully considering data splitting and validation, optimizes model performance while mitigating the risk of over-fitting. Upon evaluation, the verified model achieves desirable accuracy on the test set, demonstrating its potential for deployment in real medical settings to assist in the detection and diagnosis of PCOS. This project clearly emphasizes the use of deep learning methodologies as an effective tool for medical image analysis and thus points out the fact that the efficiency of such advanced techniques must be explored to reach the quickest and accurate diagnosis and provide better results in the healthcare field.
