Deep Learning Based CNN Model for Automated Detection of Pneumonia from Chest XRay Images

TL;DR

This paper presents a custom CNN model for automated pneumonia detection from chest X-ray images, achieving high accuracy with a lightweight architecture optimized for medical image textures.

Contribution

It introduces a novel CNN architecture using depthwise separable convolutions tailored for grayscale medical images, improving efficiency and accuracy over transfer learning models.

Findings

High precision in pneumonia detection

Efficient model with minimal computational requirements

Effective handling of class imbalance

Abstract

Pneumonia has been one of the major causes of morbidities and mortality in the world and the prevalence of this disease is disproportionately high among the pediatric and elderly populations especially in resources trained areas Fast and precise diagnosis is a prerequisite for successful clinical intervention but due to inter observer variation fatigue among experts and a shortage of qualified radiologists traditional approaches that rely on manual interpretation of chest radiographs are frequently constrained To address these problems this paper introduces a unified automated diagnostic model using a custom Convolutional Neural Network CNN that can recognize pneumonia in chest Xray images with high precision and at minimal computational expense In contrast like other generic transfer learning based models which often possess redundant parameters the offered architecture uses a tailor made depth wise separable convolutional design which is optimized towards textural characteristics of grayscale medical images Contrast Limited Adaptive Histogram Equalization CLAHE and geometric augmentation are two significant preprocessing techniques used to ensure that the system does not experience class imbalance and is more likely to generalize The system is tested using a dataset of 5863 anterior posterior chest Xrays.