Hybrid deep learning assisted chest X-ray image segmentation and classification for tuberculosis disease diagnosis

Article type: Research Article

Authors: Tiwari, Ajay^* | Katiyar, Alok

Affiliations: School of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India

Correspondence: [*] Corresponding author: Ajay Tiwari, School of Computer Science and Engineering, Galgotias University, Uttar Pradesh, India. E-mail: [email protected].

Abstract: Tuberculosis (TB) is an airborne infection affected by Mycobacterium TB. It is vital to identify cases of TB quickly if left untreated; there exists a 70% possibility of a patient dying in 10 years. An essential for extra device has been enhanced in mid to low-income countries because of the growth of automation in the field of medical care. The already restricted resources are being greatly assigned to control other dangerous infections. Modern digital radiography (DR) machines, utilized to screen chest X-rays (CXR) of possible TB victims. Combined with computer-aided detection (CAD) with the support of artificial intelligence (AI), radiologists employed in this domain actual support possible cases. This study presents a Hybrid Deep Learning Assisted Chest X-Ray Image Segmentation and Classification for Tuberculosis (HDL-ISCTB) diagnosis. The HDL-ISCTB model performs Otsu’s thresholding, which segments the lung regions from the input images. It effectually discriminates the lung areas from the background, decreasing computational complexity and potential noise. Besides, the segmented lung regions are then fed into the CNN-LSTM architecture for classification. The CNN-LSTM model leverages the powerful feature extraction capabilities of CNNs and the temporal dependencies captured by LSTM to obtain robust representations from sequential CXR image data. A wide experiments are conducted to calculate the performance of the presented approach in comparison to recent methods.

Keywords: Tuberculosis, chest x-ray images, deep learning, image segmentation, machine learning

DOI: 10.3233/IDT-230286

Journal: Intelligent Decision Technologies, vol. 18, no. 1, pp. 561-569, 2024