The Crossbreed Invariant Optimization MSVM Method for Effective Diagnosis of Pneumonia from Chest X-Ray Images

Abstract

The survival percentage of pulmonary sufferers can be improved if pneumonia is detected in time. Imaging of the chest x-Ray is the most common way of finding as well as identifying pneumonia. A competent radiologist poses a severe problem while identifying pneumonia using CXR scans. To maximize classification precision, it requires an autonomous computer-aided detection approach. Designing a lightweight autonomous pneumonia detection mechanism for resource-efficient healthcare devices is critical for enhancing healthcare quality while lowering expenses and increasing reaction time. In this proposed work, a machine learning-based hybridization approach is implemented for the identification of pneumonia in the chest x-Ray scans. The proposed methodology is divided into different segments: the 1st segment is to remove noise from the chest x-Ray scans (pre-processing). After the pre-processing of CXR scans, the second module is to extract features from the pre-processed scans. The scale-invariant feature transform (SIFT) method is implemented for the extraction of essential features. This CIO-MSVM (Crossbreed Invariant Optimization-MSVM) method will select the valuable feature with the help of FF (fitness function). This function will help to select the feature matrix and then implement the MSVM algorithm. It will pass the instance selected feature set to the train model and test model. It will classify the feature sets. If feature sets will match then detect or classify the Chest X-ray image and evaluate the performance metrics such as accuracy, spec, sens., etc and compared with the existing methods.