Detection of multi-class lung diseases based on customized neural network

Abstract

In the medical image processing domain, deep learning methodologies have outstanding performance for disease classification using digital images such as X-rays, magnetic resonance imaging (MRI), and computerized tomography (CT). However, accurate diagnosis of disease by medical personnel can be challenging in certain cases, such as the complexity of interpretation and non-availability of expert personnel, difficulty at pixel-level analysis, etc. Computer-aided diagnostic (CAD) systems with proper training have shown the potential to enhance diagnostic accuracy and efficiency. With the exponential growth of medical data, CAD systems can analyze and extract valuable information by assisting medical personnel during the disease diagnostic process. To overcome these challenges, this research introduces CX-RaysNet, a novel deep-learning framework designed for the automatic identification of various lung disease classes in digital chest X-ray images. The core novelty of the CX-RaysNet framework lies in the integration of both convolutional and group convolutional layers, along with the usage of small filter sizes and the incorporation of dropout regularization. This phenomenon helps us optimize the model's ability to distinguish minute features that reveal different lung diseases. Additionally, data augmentation techniques are implemented to augment the training and testing datasets, which enhances the model's robustness and generalizability. The performance evaluation of CX-RaysNet reveals promising results, with the proposed model achieving a multi-class classification accuracy of 97.25%. Particularly, this study represents the first attempt to optimize a model specifically for low-power embedded devices, aiming to improve the accuracy of disease detection while minimizing computational resources.