COMPARATIVE ANALYSIS OF CUSTOMIZED CNN AND TEACHABLE MACHINE FOR PNEUMONIA DETECTION IN CHEST X-RAY IMAGES - Dave, Parth R.; Patel, Yogesh B.; Patel, Shivangkumar R. (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Pneumonia is a serious and potentially life-threatening lung infection that necessitates prompt and accurate diagnosis to ensure effective treatment and reduce the risk of complications or fatalities. Traditional diagnosis through chest X-ray interpretation can be challenging due to variability in radiologist expertise and the subtle nature of some pneumonia-related abnormalities. With the rise of artificial intelligence in healthcare, deep learning techniques—particularly Convolutional Neural Networks (CNNs)—have shown great promise in automating and enhancing diagnostic accuracy in medical imaging. This study conducts a comparative evaluation of a custom-built CNN model and Google’s Teachable Machine, a user-friendly no-code machine learning platform, for binary classification of chest X-ray images into two categories: "Normal" and "Pneumonia." Both models were trained and tested on the same dataset to ensure a fair comparison. The experimental outcomes reveal that the scratch-built CNN model significantly outperforms the Teachable Machine model in key performance metrics including accuracy, recall, precision, and F1-score. These results highlight the superiority of tailored deep learning architectures designed specifically for the complexities of medical image analysis. The findings suggest that while generalized machine learning tools offer accessibility, they may fall short in critical diagnostic tasks where specialized models can better capture domain-specific features for improved clinical decision-making.