Hybrid Optimized Deep Learning-Based Bacilli Segmentation and Infection-Level Identification of Tuberculosis Using Sputum Images

Abstract

Presently, one of the foremost health issues and an extremely transferrable disease is Tuberculosis which is spreading worldwide. Tuberculosis is generally produced by mycobacterium tuberculosis and can cause death if it is not detected at premature stages. Therefore, a precise and efficient approach is essential for the identification of tuberculosis. The physical analysis of sputum smears through fluorescence microscopy for the detection process and also the treatment process is a subjective and difficult task. Thus, the hybrid optimization-driven deep learning approach is designed in this paper for infection-level identification of severity prediction of tuberculosis. Here, a sputum image is considered for performing tuberculosis detection and it is acquired from a database. For executing bacilli segmentation, the Deep Contour Aware Network (DCAN) model is used. In addition, DCAN is trained by the introduced Fractional Artificial Hummingbird Algorithm (FAHA). Additionally, the LeNet scheme is applied to detect the infection level and severity detection. The LeNet is also trained using Coot-FAHA thereby; the system performance is highly increased. The devised optimization-enabled deep learning method achieved improved performance with precision, F1-score, and recall of 0.9326, 0.9386, and 0.9163, respectively.