Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification

Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack of diagnosis and appropriate management. The diagnosis of PCOD, along with proper classification with the help of ultrasound imaging, would be of immense importance for early intervention and timely management of the condition. However, most of the existing approaches suffer from lots of problems, including low accuracy and capability in feature extraction, and may also be resilient to noise; it can further delay or lead to a wrong diagnosis. The main objective of this paper is to address these important issues by proposing a deep learning model, Holographic Convolutional Dense Network (Coco-HoloNet) that will be tailored for the precise detection and classification of PCOD in ultrasound images with high accuracy. These are multi-fold contributions which focus on improvement in diagnostic accuracy by overcoming the various limitations of conventional approaches. CoCo-HoloNet is using a layered architecture by integrating convolutional layers, dense blocks, and pooling strategies that leverage capturing and extraction of significant features from the input effectively. More importantly, the model is also embedded with the Tangent-Runner Adaptive Optimization (TRAdO) technique, which dynamically calculates the regularization parameters to overcome overfitting problems and improves the generalization capability of the model. The approach not only ensures the richest possible feature representation, but it also results in outstanding improvements within the performance measures of a model, such that the accuracy rate exceeds 99%. Further experimentation with CoCo-HoloNet on an extended Kaggle PCOD ultrasound image dataset proves its effectiveness by reporting higher precision, recall, and F1-scores than those obtained by state-of-the-art existing methods.