An Optimal Model Combining SqueezeNet and Machine Learning Methods for Lung Disease Diagnosis

Artificial intelligence (AI) is rapidly evolving in healthcare, with transformative potential. AI revolutionizes medical imaging by enabling online self-diagnosis for patients and improving diagnostic accuracy for healthcare professionals. While valuable datasets aid machine learning in disease detection, challenges persist in diagnosing similar lung conditions from chest X-rays. Integrating AI into healthcare holds promise for enhanced outcomes and efficiency.

In this article, we aim to present a new AI model that solves this challenge by allowing the differentiation, diagnosis and classification of three distinct diseases, whose symptoms are very similar. The fundamental contribution is to reduce the number of parameters used while maintaining the same level of precision for use in embedded systems.

Our proposed model combines the power of the neural network using the SqueezeNet architecture with a set of machine learning algorithms as classifiers, including logistic regression, support vector machine (SVM), k-nearest neighbors (KNN), decision tree, and naive Bayes. The chest Xray dataset used in the proposed model consists of CXR images that are classified into four categories: pneumonia, tuberculosis, COVID-19, and normal cases.

Our proposed model demonstrated remarkable accuracy (97,32%), precision (97,33), F1 score (97,31%), recall (97,30%), and AUC (99,40), which is close to the best model. Whereas, the number of parameters used by our model (4,6 M) is very small compared to the best model in the literature (47M).

The model demonstrated good classification accuracy. In addition, the proposed model has the ability to use fewer parameters, which means it requires less internal memory and computing resources.