Residual-Concatenate Neural Network with Deep Regularization Layers for Binary Classification

TL;DR

This paper introduces a deep neural network with residual and concatenation layers combined with regularization techniques, achieving high accuracy in diagnosing Polycystic Ovary Syndrome.

Contribution

It presents a novel deep neural network architecture that integrates residual and concatenation processes with regularization layers for improved binary classification.

Findings

Achieved 99.3% accuracy in PCOS diagnosis

Demonstrated effectiveness of deep regularized residual-concatenate architecture

Improved model robustness through layered regularization

Abstract

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.