Classification of tuberculosis with SURF spatial pyramid features

Alfadhli, Fares Hasan Obaid; Mand, Ali Afzalian; Sayeed, Shohel; Sim, K. S.; Al-Shabi, Mundher

doi:10.1109/icoras.2017.8308044

Cited by 11 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors then used SVM [22] for classification and reported accuracy of 94.2% and 86.0% with gist and HOG features, respectively. Alfadhli et al used SURF [7] for features calculation from varied window sizes used as input for SVM based classifier achieving 89% area under the curve (AUC) [15]. Hogeweg et al presented a feature extraction and classification mechanism to compute local pixel characteristics.…”

Section: A Machine Learning Based Tb Diagnosismentioning

confidence: 99%

“…The identification accuracy is used to gauge the performance of proposed TB identification methodology based on B-CNN in comparison with other techniques from literature including: SVM [15], AlexNet [12], VGG16 [26], VGG19 [26], ResNet [42], and CNN based methodologies proposed by Lopes and Valiati [25] and Sivaramakrishnan et al [17]. For the experimentation and comparison, three baseline CNN architectures are proposed, which are presented in Table 2 including Architecture-1, Architecture-2, and Architecture-3.…”

Section: A Accuracy Of Tb Identificationmentioning

confidence: 99%

“…The main problem is that uncertainty factor is not considered for TB diagnosis in most of the schemes including [15]- [17]. In case of higher values of uncertainty, the decision should be verified by the radiologists.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty. INDEX TERMS Tuberculosis identification, computer-aided diagnostics, medical image analysis, Bayesian convolutional neural networks, model uncertainty.

show abstract

Section: A Machine Learning Based Tb Diagnosismentioning

confidence: 99%

Section: A Accuracy Of Tb Identificationmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Several CAD methods are presented in the literature for diagnosing pulmonary TB in CXR images. To make a fair comparison, we considered the following state-of-the-art methods [ 14 , 15 , 17 , 21 , 22 , 41 , 42 ], because these approaches selected the same data sets and experimental protocols as considered in our study. Moreover, in some recent studies [ 21 ], the authors adopted existing CNN models to classify the different types of pulmonary abnormalities including TB.…”

Section: Resultsmentioning

confidence: 99%

“…Whereas in LBP histogram parameters, cell size was selected as 1 × 1 by applying L2-normalization to each LBP cell histogram. Thus, our comparative analysis was more detailed than the various existing studies [ 14 , 17 , 21 , 22 ]. For the MC data set, the performance gain of our model in contrast to Govindarajan and Swaminathan [ 15 ] (second-best) was greater than 4.4%, 5%, and 2.5% for AR, ACC, and AUC, respectively.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study

et al. 2020

View full text Add to dashboard Cite

Background Tuberculosis (TB) is one of the most infectious diseases that can be fatal. Its early diagnosis and treatment can significantly reduce the mortality rate. In the literature, several computer-aided diagnosis (CAD) tools have been proposed for the efficient diagnosis of TB from chest radiograph (CXR) images. However, the majority of previous studies adopted conventional handcrafted feature-based algorithms. In addition, some recent CAD tools utilized the strength of deep learning methods to further enhance diagnostic performance. Nevertheless, all these existing methods can only classify a given CXR image into binary class (either TB positive or TB negative) without providing further descriptive information. Objective The main objective of this study is to propose a comprehensive CAD framework for the effective diagnosis of TB by providing visual as well as descriptive information from the previous patients’ database. Methods To accomplish our objective, first we propose a fusion-based deep classification network for the CAD decision that exhibits promising performance over the various state-of-the-art methods. Furthermore, a multilevel similarity measure algorithm is devised based on multiscale information fusion to retrieve the best-matched cases from the previous database. Results The performance of the framework was evaluated based on 2 well-known CXR data sets made available by the US National Library of Medicine and the National Institutes of Health. Our classification model exhibited the best diagnostic performance (0.929, 0.937, 0.921, 0.928, and 0.965 for F1 score, average precision, average recall, accuracy, and area under the curve, respectively) and outperforms the performance of various state-of-the-art methods. Conclusions This paper presents a comprehensive CAD framework to diagnose TB from CXR images by retrieving the relevant cases and their clinical observations from the previous patients’ database. These retrieval results assist the radiologist in making an effective diagnostic decision related to the current medical condition of a patient. Moreover, the retrieval results can facilitate the radiologists in subjectively validating the CAD decision.

show abstract

A semantic contour based segmentation of lungs from chest x‐rays for the classification of tuberculosis using Naïve Bayes classifier

Pavani¹,

Biswal²,

Sairam³

et al. 2021

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Tuberculosis (TB) is an infectious disease that primarily affects the lungs. If the TB is left undiagnosed in its early stage, it may affect other body parts leading to sudden death. A posterior anterior chest radiographs are used to diagnose pulmonary TB. This work presents a novel automatic system for the early detection of pulmonary TB. Initially, the Chan-Vese active contour model is generated to segment the lung region from the preprocessed chest x-rays. Different textural, statistical, and morphological features are extracted from the segmented lungs. Finally, the pertinent feature vector is considered for the classification of chest x-rays using the Naïve Bayes classifier (NBC). The NBC is trained using 10-fold cross validation technique. The proposed method achieves an average accuracy, the area under the curve, specificity, and sensitivity as 95.5%, 98%, 93.3%, and 94.6% respectively, when tested on different available datasets. Comparison results reveals that the Naïve Bayes classifier outperforms than the multinomial Naïve Bayes classifier and Bernoulli's Naïve Bayes classifier in classifying the chest x-rays into healthy and TB.

show abstract

Classification of tuberculosis with SURF spatial pyramid features

Cited by 11 publications

References 13 publications

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks

Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study

A semantic contour based segmentation of lungs from chest x‐rays for the classification of tuberculosis using Naïve Bayes classifier

Contact Info

Product

Resources

About