An efficient method of multicolor detection using global optimum thresholding for image analysis

Goyal, Lalit Mohan; Mittal, Mamta; Kumar, Munish; Kaur, Bhavneet; Sharma, Meenakshi; Kaur, Iqbaldeep

doi:10.1007/s11042-020-10365-y

Cited by 5 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These advancements were made possible by deep learning's ability to learn features from data rather than hand-designed features based on domain-specific information. Presently, the medical field is one which requires utmost care and investigation, with medical sciences at a level that necessitates substantial research and technical proposals in order to address the increasingly complicated medical challenges [13]. Deep learning is rapidly becoming the industry standard, leading in improved outcomes in a wide range of medical applications.…”

Section: Related Workmentioning

confidence: 99%

Histogram Matched Chest X-Rays Based Tuberculosis Detection Using CNN

Ignatius¹,

Selvakumar²,

Paul³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Tuberculosis (TB) is a severe infection that mostly affects the lungs and kills millions of people's lives every year. Tuberculosis can be diagnosed using chest X-rays (CXR) and data-driven deep learning (DL) approaches. Because of its better automated feature extraction capability, convolutional neural networks (CNNs) trained on natural images are particularly effective in image categorization. A combination of 3001 normal and 3001 TB CXR images was gathered for this study from different accessible public datasets. Ten different deep CNNs (Resnet50, Resnet101, Resnet152, InceptionV3, VGG16, VGG19, DenseNet121, DenseNet169, DenseNet201, MobileNet) are trained and tested for identifying TB and normal cases. This study presents a deep CNN approach based on histogram matched CXR images that does not require object segmentation of interest, and this coupled methodology of histogram matching with the CXRs improves the accuracy and detection performance of CNN models for TB detection. Furthermore, this research contains two separate experiments that used CXR images with and without histogram matching to classify TB and non-TB CXRs using deep CNNs. It was able to accurately detect TB from CXR images using pre-processing, data augmentation, and deep CNN models. Without histogram matching the best accuracy, sensitivity, specificity, precision and F1-score in the detection of TB using CXR images among ten models are 99.25%, 99.48%, 99.52%, 99.48% and 99.22% respectively. With histogram matching the best accuracy, sensitivity, specificity, precision and F1-score are 99.58%, 99.82%, 99.67%, 99.65% and 99.56% respectively. The proposed methodology, which has cutting-edge performance, will be useful in computer-assisted TB diagnosis and aids in minimizing irregularities in TB detection in developing countries.

show abstract

Section: Related Workmentioning

confidence: 99%