Using Teaching Learning-Based Optimization with Convolutional Neural Network to Detect Pneumonia Based On Chest X-Ray Images

Chest X-ray imaging plays a vital role in the treatment of respiratory diseases such as pneumonia. Recent technological innovations have significantly improved the efficiency of the image analysis process especially Artificial and convolutional neural networks. However, there is a need to improve the accuracy. Thus, it is important to develop an automated, early diagnosis system that can deliver quick decisions and significantly lower diagnosis error. Recent advancements in emerging Artificial Intelligence approaches, particularly Deep Learning algorithms, have made the chest X-ray screening a viable option for early Pneumonia detection. Therefore, this paper focuses on using Teaching Learning Based Optimization (TLBO) with Convolutional Neural Network (CNN) to improve the accuracy of detecting pneumonia in Chest X-ray images. The research study was conducted on the chest X-ray images of the pneumonia data set and is compared with previous work. Results confirm that TLBO with CNN is a good choice for detecting pneumonia at the accuracy of 98.88% and is an improvement over benchmark studies.