Strid-CNN: moving filters with convolution neural network for multi-class pneumonia classification

Abstract

Millions of people around the world suffer from pneumonia, a serious lung illness. To effectively treat and manage this condition, a quick and accurate diagnosis is essential. This study thoroughly examines different ways of using transfer learning to classify pneumonia into multiple categories. We use well-known methods like DenseNet121, VGGNet-16, ResNet-50, and Inception Net, as well as a new method called Strid-CNN, which applies moving filters with convolution neural network. Through extensive testing, we show that each method effectively uses pre-learned information on a large dataset of medical images, accurately identifying pneumonia across various classes. Our results reveal subtle differences in performance among these methods, providing insights into how well they adapt to the challenging field of medical image analysis. Additionally, the Strid-CNN method shows promising results, indicating its potential as a competitive alternative. This research offers valuable guidance on choosing the right transfer learning approach for classifying pneumonia into multiple categories, contributing to improvements in diagnostic accuracy and healthcare effectiveness. Our study not only highlights the current state of transfer learning in pneumonia classification but also its potential to enhance clinical outcomes and patient care.