Ensemble self-attention technology for improving the accuracy and efficiency of lung disease diagnosis

Article type: Research Article

Authors: Shafi, Smd | Sathiya Kumar, C.^{; *}

Affiliations: School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

Correspondence: [*] Corresponding author. C. Sathiya Kumar, E-mail: [email protected].

Abstract: Identifying diseases using chest X-rays is challenging because more medical professionals are needed. A chest X-ray contains many features, making it difficult to pinpoint the factors causing a disease. Moreover, healthy individuals are more common than those with illnesses, and various diseases occur at different rates. To diagnose the disease accurately using X-ray images, extracting significant features and addressing unbalanced data is essential. To resolve these challenges, a proposed ensemble self-attention-based deep neural network aims to tackle the problem of unbalanced information distribution by creating a new goal factor. Additionally, the InceptionV3 architecture is trained to identify significant features. The proposed objective function is a performance metric that adjusts the ratio of positive to negative instances, and the suggested loss function can dynamically mitigate the impact of many negative observations by reducing each cross-entropy term by a variable amount. Tests have shown that ensemble self-attention performs well on the ChestXray14 dataset, especially regarding the dimension around the recipient’s characteristics curves.

Keywords: Deep neural networks, cross-weighted entropy loss, data with discrepancies, feature extraction, X-ray

DOI: 10.3233/JIFS-236444

Journal: Journal of Intelligent & Fuzzy Systems, vol. Pre-press, no. Pre-press, pp. 1-16, 2024