Faculty Publications
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Item A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images(wiley, 2021) Panicker, R.O.; Pawan, S.J.; Rajan, J.; Sabu, M.K.This chapter describes a lightweight convolutional neural network model that automatically detects Tuberculosis (TB) bacilli from sputum smear microscopic images. According to WHO, about onefourth of the population in the universe is infected with TB, and every day five thousand people are killed due to TB disease. There are well-known recommended diagnostics are available for TB detection, among them sputum smear microscopic examination is a primary and most efficient recommended method for most of the developing and moderately developed countries. However, this manual detection method is highly error-prone and time-consuming. In this chapter, we proposed a lightweight CNN model for classifying Tuberculosis bacilli from non-bacilli objects. We adopted a Convolutional Neural Network (CNN) architecture with a skip connection of variable lengths that can identify TB bacilli from sputum smear microscopic images. The performance of the proposed model in terms of accuracy is close to the state-of-the-art. However, the number of parameters in the proposed model is significantly less than other recently proposed models. © 2021 Scrivener Publishing LLC.Item A comparative study of different auto-focus methods for mycobacterium tuberculosis detection from brightfield microscopic images(Institute of Electrical and Electronics Engineers Inc., 2016) Saini, G.; Panicker, R.O.; Soman, B.; Rajan, J.Automatic tuberculosis (TB) detection methods using microscopic images are becoming more popular now a days. Auto-focusing is the first and foremost step in the development of an automated microscope for TB detection. Different focus measures exist for the selection of in-focus image from both fluorescence and bright field microscopic images. Recently, some researchers have investigated and compared several different focus measures for TB sputum microscopy. In this study we focused on bright field microscopic images and considered around 20 popular focus measures. Experiments were conducted on a large set of images having different features. © 2016 IEEE.Item A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Panicker, R.O.; Soman, B.; Saini, G.; Rajan, J.Tuberculosis (TB) is an infectious disease caused by the bacteria Mycobacterium tuberculosis. It primarily affects the lungs, but it can also affect other parts of the body. TB remains one of the leading causes of death in developing countries, and its recent resurgences in both developed and developing countries warrant global attention. The number of deaths due to TB is very high (as per the WHO report, 1.5 million died in 2013), although most are preventable if diagnosed early and treated. There are many tools for TB detection, but the most widely used one is sputum smear microscopy. It is done manually and is often time consuming; a laboratory technician is expected to spend at least 15 min per slide, limiting the number of slides that can be screened. Many countries, including India, have a dearth of properly trained technicians, and they often fail to detect TB cases due to the stress of a heavy workload. Automatic methods are generally considered as a solution to this problem. Attempts have been made to develop automatic approaches to identify TB bacteria from microscopic sputum smear images. In this paper, we provide a review of automatic methods based on image processing techniques published between 1998 and 2014. The review shows that the accuracy of algorithms for the automatic detection of TB increased significantly over the years and gladly acknowledges that commercial products based on published works also started appearing in the market. This review could be useful to researchers and practitioners working in the field of TB automation, providing a comprehensive and accessible overview of methods of this field of research. © 2015, Springer Science+Business Media New York.Item An adoption model describing clinician’s acceptance of automated diagnostic system for tuberculosis(Springer Verlag service@springer.de, 2016) Panicker, R.O.; Soman, B.; Gangadharan, K.V.; Sobhana, N.V.Computerised medical diagnosing systems are very important to all healthcare professionals, especially clinicians who help in clinical decision-making in complex situations. The acceptance of automated or computerised medical diagnosing system for Tuberculosis (TB) among clinicians is very essential for its effective implementation and usage. This paper proposes a framework that aims to examine factors that influence clinician’s acceptance and use of computerised TB detection system. An extended Unified Theory of Acceptance and Use of Technology (UTAUT) model is adopted in the healthcare context of a developing country for this purpose. The proposed framework is expected to help researchers and clinicians to assess the uptake of modern technology by health care professionals and the tool could be used in other healthcare contexts also. This paper also reviewed previous research adopting UTAUT model, for identifying the constructs promoting the adoption of technology acceptance in health care context. © 2016, IUPESM and Springer-Verlag Berlin Heidelberg.Item Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods(PWN-Polish Scientific Publishers bbe@ibib.waw.pl, 2018) Panicker, R.O.; Kalmady, K.S.; Rajan, J.; Sabu, M.K.An automatic method for the detection of Tuberculosis (TB) bacilli from microscopic sputum smear images is presented in this paper. According to WHO, TB is the ninth leading cause of death all over the world. There are various techniques to diagnose TB, of which conventional microscopic sputum smear examination is considered to be the gold standard. However, the aforementioned method of diagnosis is time intensive and error prone, even in experienced hands. The proposed method performs detection of TB, by image binarization and subsequent classification of detected regions using a convolutional neural network. We have evaluated our algorithm using a dataset of 22 sputum smear microscopic images with different backgrounds (high density and low-density images). Experimental results show that the proposed algorithm achieves 97.13% recall, 78.4% precision and 86.76% F-score for the TB detection. The proposed method automatically detects whether the sputum smear images is infected with TB or not. This method will aid clinicians to predict the disease accurately in a short span of time, thereby helping in improving the clinical outcome. © 2018 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences