Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification
This work addresses a specific medical diagnosis problem, but it appears incremental as it applies known techniques like regularization and residual connections to a new dataset.
The paper tackled binary classification for Polycystic Ovary Syndrome diagnosis by training a deep neural network with residual and concatenation processes and deep regularization layers, achieving an accuracy of 99.3%.
Many complex Deep Learning models are used with different variations for various prognostication tasks. The higher learning parameters not necessarily ensure great accuracy. This can be solved by considering changes in very deep models with many regularization based techniques. In this paper we train a deep neural network that uses many regularization layers with residual and concatenation process for best fit with Polycystic Ovary Syndrome Diagnosis prognostication. The network was built with improvements from every step of failure to meet the needs of the data and achieves an accuracy of 99.3% seamlessly.