Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network

TLDR

Chest radiographs are the first‑line tool for detecting major lung diseases such as tuberculosis, pneumonia, and lung cancer, yet millions die each year from late diagnosis, underscoring the need for early detection. The study aims to develop an ANN‑based system that segments lungs, extracts features, and classifies chest radiographs to detect tuberculosis, lung cancer, and pneumonia. The method employs intensity‑ and discontinuity‑based segmentation to delineate lung boundaries, extracts statistical and geometrical features, and classifies the images with a feed‑forward back‑propagation neural network.

Abstract

Chest Radiograph is the preliminary requirement for the identification of lung diseases. Tuberculosis; pneumonia and lung cancer these lung diseases are major health threat. According to recent survey; which was given by WHO; rate of people dying due to late diagnosis of lung diseases is in millions. Early diagnosis of these diseases can curb mortality rate. This paper proposes lung segmentation; lung feature extraction and it's classification using artificial neural network technique for the detection of lung diseases such as TB; lung cancer and pneumonia. We have used the simple image processing techniques like intensity based method and discontinuity based method to detect lung boundaries. Statistical and geometrical features are extracted. Image classification using feed forward and back propagation neural network to detect major lung diseases.