A novel modified SFTA approach for feature extraction

Hasan, Junayed; Uddin, Jia; Pinku, Subroto Nag

doi:10.1109/ceeict.2016.7873115

Cited by 11 publications

(10 citation statements)

References 16 publications

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“…The dataset used here contains two datasets: 138 X-ray images, including 58 TB infected cases, and 662 X-ray images from Shenzhen Hospital with 336 TB cases. Several studies reported the application of artificial intelligence and DL for the case of healthcare, including COVID-19 [27][28][29][30][31][32][33][34][35][36][37][38]. One interesting study compared a multilayer network technique with a single network for the case of COVID-19 vaccinations; however, they did not consider disease diagnosis [35].…”

Section: Related Workmentioning

confidence: 99%

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

et al. 2023

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Due to its high transmissibility, the COVID-19 pandemic has placed an unprecedented burden on healthcare systems worldwide. X-ray imaging of the chest has emerged as a valuable and cost-effective tool for detecting and diagnosing COVID-19 patients. In this study, we developed a deep learning model using transfer learning with optimized DenseNet-169 and DenseNet-201 models for three-class classification, utilizing the Nadam optimizer. We modified the traditional DenseNet architecture and tuned the hyperparameters to improve the model’s performance. The model was evaluated on a novel dataset of 3312 X-ray images from publicly available datasets, using metrics such as accuracy, recall, precision, F1-score, and the area under the receiver operating characteristics curve. Our results showed impressive detection rate accuracy and recall for COVID-19 patients, with 95.98% and 96% achieved using DenseNet-169 and 96.18% and 99% using DenseNet-201. Unique layer configurations and the Nadam optimization algorithm enabled our deep learning model to achieve high rates of accuracy not only for detecting COVID-19 patients but also for identifying normal and pneumonia-affected patients. The model’s ability to detect lung problems early on, as well as its low false-positive and false-negative rates, suggest that it has the potential to serve as a reliable diagnostic tool for a variety of lung diseases.

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Section: Related Workmentioning

confidence: 99%

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

et al. 2023

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“…Accuracy is the percentage of correctly classified instances, or in other words, the ratio of the true results to the total number of cases examined [42,43]. This factor cannot differentiate between FN and FP error and considers them the same.…”

Section: -1-accuracymentioning

confidence: 99%

Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN

Sadeghi,

Mahdavi-Nasab,

Zeinali

et al. 2023

jist

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Semantic segmentation is a branch of computer vision, used extensively in image search engines, automated driving, intelligent agriculture, disaster management, and other machine-human interactions. Semantic segmentation aims to predict a label for each pixel from a given label set, according to semantic information. Among the proposed methods and architectures, researchers have focused on deep learning algorithms due to their good feature learning results. Thus, many studies have explored the structure of deep neural networks, especially convolutional neural networks. Most of the modern semantic segmentation models are based on fully convolutional networks (FCN), which first replace the fully connected layers in common classification networks with convolutional layers, getting pixel-level prediction results. After that, a lot of methods are proposed to improve the basic FCN methods results. With the increasing complexity and variety of existing data structures, more powerful neural networks and the development of existing networks are needed. This study aims to segment a high-resolution (HR) image dataset into six separate classes. Here, an overview of some important deep learning architectures will be presented with a focus on methods producing remarkable scores in segmentation metrics such as accuracy and F1-score. Finally, their segmentation results will be discussed and we would see that the methods, which are superior in the overall accuracy and overall F1-score, are not necessarily the best in all classes. Therefore, the results of this paper lead to the point to choose the segmentation algorithm according to the application of segmentation and the importance degree of each class.

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“…Using features that do not have much impact on finding the proper time history trend of crack occurrence can not only increase the computational complexity but can also introduce a fallacious trend, resulting in unintended and untimely alarms. Therefore, we need a feature selection algorithm to serve our purpose [37]. We incorporated the Boruta feature selector algorithm to sort out the useful features for calculating MDs.…”

Section: Boruta-mahalanobis Systemmentioning

confidence: 99%

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

Habib

Kim

2020

Applied Sciences

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This study aims at characterizing crack types for reinforced concrete beams through the use of acoustic emission burst (AEB) features. The study includes developing a solid crack assessment indicator (CAI) accompanied by a crack detection method using the k-nearest neighbor (k-NN) algorithm that can successfully distinguish among the normal condition, micro-cracks, and macro-cracks (fractures) of concrete beam test specimens. Reinforced concrete (RC) beams undergo a three-point bending test, from which acoustic emission (AE) signals are recorded for further processing. From the recorded AE signals, crucial AEB features like the rise time, decay time, peak amplitude, AE energy, AE counts, etc. are extracted. The Boruta-Mahalanobis system (BMS) is utilized to fuse these features to provide us with a comprehensive and reliable CAI. The noise from the CAI is removed using the cumulative sum (CUMSUM) algorithm, and the final CAI plot is used to classify the three different conditions: normal, micro-cracks, and fractures using k-NN. The proposed method not only for the first time uses the entire time history to create a reliable CAI, but it can meticulously distinguish between micro-cracks and fractures, which previous works failed to deal with in a precise manner. Results obtained from the experiments display that the CAI built upon AEB features and BMS can detect cracks occurring in early stages, along with the gradually increasing damage in the beams. It also soundly outperforms the existing method by achieving an accuracy (classification) of 99.61%, which is 17.61% higher than the previously conducted research.

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A novel modified SFTA approach for feature extraction

Cited by 11 publications

References 16 publications

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Rethinking Densely Connected Convolutional Networks for Diagnosing Infectious Diseases

Comparing the Semantic Segmentation of High-Resolution Images Using Deep Convolutional Networks: SegNet, HRNet, CSE-HRNet and RCA-FCN

A Crack Characterization Method for Reinforced Concrete Beams Using an Acoustic Emission Technique

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