Tuberculosis mycobacterium segmentation using deeply connected membership tweaked fuzzy segmentation network

Tuberculosis (TB) is a contagious disease that spreads through the air when an infected person coughs, sneezes, or talks. TB is a bacterial infection caused by Mycobacterium tuberculosis (MTB). Nowadays, TB diagnosis is mainly influenced by the segmentation of MTB objects in Ziehl-Neelsen (ZN)-stained microscopy images. There are a number of segmentation algorithms that have been extensively examined in the literature, and these are still prone to issues like less accuracy due to over and under segmentation as well as unclear edges in bacterium objects. Hence, this paper presents a novel MTB segmentation method entitled ‘TB Mycobacterium Segmentation using Deeply connected Membership tweaked Fuzzy Segmentation Network (TBMS-DMFSN)’. This method takes the ZN-stained MTB color microscopic image as input and generates the segmented result. The own contribution of this paper is the FCM-based Mycobacterium segmentation algorithm called ‘Deeply connected Membership modified Fuzzy Segmentation Network (DMFSN),’ which is a deeply connected fuzzy network. The DMFSN network uses a back-propagated fuzzy network to separate the Mycobacterium objects. The proposed TBMS-DMFSN method incorporates eight optimum feature images from four different color spaces, lightness enhancement, and Gamma correction algorithms. Experimental evaluation is constructed with the help of both online and real-time clinical databases. The proposed TBMS-DMFSN approach has overall average segmentation accuracy (SA) of 95.26%, whereas the second-best method has a segmentation accuracy of 93.03%. As a result, the proposed approach raises the SA values by up to 2.226%. The achieved results reflect that the proposed method produces higher accuracy compared to state-of-the-art methods.