IMPACT OF COMMONLY USED OPTIMIZERS AND LEARNING RATE ON THE PERFORMANCE OF THE TRANSFER LEARNING MODEL DURING THE DETECTION OF PNEUMONIA.

In recent years, deep learning techniques have shown remarkable success in various medical image analysis tasks, including pneumonia detection. Transfer learning, leveraging pretrained models such as VGG16, has been particularly effective due to its ability to generalize features from large datasets. However, the choice of optimizer and learning rate significantly impacts model performance and convergence speed. This study investigates the impact of three popular optimizers (Adam, RMSProp, and SGD) and different learning rates on pneumonia detection using the VGG16 transfer learning model. Pneumonia is a prevalent infectious disease with significant mortality worldwide. Early and accurate detection plays a crucial role in patient management and treatment outcomes. Deep learning models, especially those based on convolutional neural networks (CNNs), have shown promise in automating pneumonia detection from chest X-ray images. Transfer learning, using pretrained CNNs like VGG16, offers a powerful approach to leverage knowledge learned from large-scale datasets. We employ a VGG16 model pretrained on ImageNet and fine-tune it on a dataset of chest X-ray images for pneumonia detection. Three optimizers, Adam, RMSProp, and SGD, are compared under various learning rates. The dataset is split into training, validation, and test sets to evaluate model performance. Each optimizer is tested with learning rates ranging from 0.1 to 0.0000001 to study their effects on training dynamics, convergence speed, and final accuracy metrics. Our experimental results demonstrate that the choice of optimizer and learning rate significantly influences the performance of the VGG16 model for pneumonia detection. SGD consistently achieves faster convergence and higher validation accuracy compared to RMSProp and Adam across all tested learning rates. The choice of learning rate within the range of 0.1 to 0.0000001 also impacts performance, with an optimal balance between convergence speed and accuracy observed around 0.0001 for SGD. These findings highlight the critical role of optimizer selection and learning rate tuning in maximizing the performance of VGG16 models in medical image analysis tasks like pneumonia detection.