Identification of Pneumonia in Chest X-Ray Images using Bio-Inspired Optimization Based LSTM
Keywords:
Pneumonia, Chest X-Ray (CXR), BIO-LSTM, Image, PatientsAbstract
Pneumonia, a potentially dangerous respiratory condition, affects millions of people worldwide. Pneumonia has to be identified as early and precisely as possible in order to successfully treat and care for patients. Although the diagnosis of pneumonia is often made via chest X-ray (CXR) imaging, doing so may be time-consuming and complicated for medical professionals. This article proposes a novel strategy for the identification of pneumococcal infection in CXR images using bio-inspired optimization based Long Short-Term Memory (BIO-LSTM) methodologies. Bio-inspired optimization techniques are used, such as Stud Genetic Algorithm (SGA) to enhance the performance of the LSTM model. CXR images were compiled into a comprehensive Kaggle dataset from some sources. To provide an accurate portrayal of the target pathophysiology, the dataset included a wide variety of pneumonia and non-pneumonia patients. The dataset received a preprocessing phase utilizing Sparse Auto-Encoder (SAE) methods to enhance its quality and utility. A popular feature extraction method is the Gabor filter, which takes inspiration from the human visual system. These algorithms optimize the LSTM design and its parameters by simulating the behavior of natural systems, such as the development of genetic populations. A dataset of CXR including both normal and pneumonia patients is utilized to assess the suggested approach. Results from experiments show that the BIO-LSTM strategy for identifying pneumonia performs better than conventional approaches. According to this study's results, pneumonia may be identified more precisely and reliably by combining BIO-LSTM algorithms with the temporal information included in CXR images.
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