Advancing COVID-19 Diagnosis with CNNs: An Empirical Study of Learning Rates and Optimization Strategies  

作　　者：Mainak Mitra Soumit Roy Mainak Mitra;Soumit Roy(Product & Program (Data Platform), Conviva, Foster City, USA;Presales Enngineering (Analytics), Jade Global, San Jose, USA)

机构地区：[1]Product & Program (Data Platform), Conviva, Foster City, USA [2]Presales Enngineering (Analytics), Jade Global, San Jose, USA

出　　处：《Intelligent Control and Automation》2023年第4期45-78,共34页智能控制与自动化（英文）

摘　　要：The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.The rapid spread of the novel Coronavirus (COVID-19) has emphasized the necessity for advanced diagnostic tools to enhance the detection and management of the virus. This study investigates the effectiveness of Convolutional Neural Networks (CNNs) in the diagnosis of COVID-19 from chest X-ray and CT images, focusing on the impact of varying learning rates and optimization strategies. Despite the abundance of chest X-ray datasets from various institutions, the lack of a dedicated COVID-19 dataset for computational analysis presents a significant challenge. Our work introduces an empirical analysis across four distinct learning rate policies—Cyclic, Step Based, Time-Based, and Epoch Based—each tested with four different optimizers: Adam, Adagrad, RMSprop, and Stochastic Gradient Descent (SGD). The performance of these configurations was evaluated in terms of training and validation accuracy over 100 epochs. Our results demonstrate significant differences in model performance, with the Cyclic learning rate policy combined with SGD optimizer achieving the highest validation accuracy of 83.33%. This study contributes to the existing body of knowledge by outlining effective CNN configurations for COVID-19 image dataset analysis, offering insights into the optimization of machine learning models for the diagnosis of infectious diseases. Our findings underscore the potential of CNNs in supplementing traditional PCR tests, providing a computational approach to identify patterns in chest X-rays and CT scans indicative of COVID-19, thereby aiding in the swift and accurate diagnosis of the virus.

关 键 词：Learning Rate AI OPTIMIZER Deep Learning CNN Multi Class Classification 

分 类 号：H31[语言文字—英语]