A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

Tallapragada, V. V. Satyanarayana; Manga, N. Alivelu; Kumar, Gulshan

doi:10.1007/s11042-023-14367-4

Cited by 7 publications

(1 citation statement)

References 41 publications

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“…Both traditional and deep learning systems have severe limitations when trying to categorizeXray images of COVID-19 [11]. As a result, more research is required to develop better and more effective techniques for COVID-19 diagnosis [12]. Finally, the detection and diagnosis of illnesses, the decrease in false diagnoses, and the outcomes for patients can all be significantly improved by the classification of COVID-19 X-ray pictures using MLA and Harr feature extraction approaches.…”

Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

T.,

Jini

2023

IJRITCC

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In this study, we investigated how effectively COVID-19 image categorization using Harr feature extraction and machine learning algorithms. We were particularly interested in the effectiveness of these algorithms. A dataset of 500 X-ray scans, equally split between 250 COVID-19-positive cases and 250 healthy controls, served as the basis for our study. K-nearest neighbors,decision tree, Linear regression, support vector machine, regression, classification, naive Bayes,random forest, as well as linear discriminant analysis were among the seven machine-learning approaches used to categorize the photos. With the use of Harr feature extraction, the features of the pictures were extracted. We studied the efficacy of COVID-19 X-ray images for classification utilizing the combination of machine learning as well as the Harr feature extraction methods in the present investigation due to their effectiveness. We searched a database of 500 X-rays for this investigation, dividing them equally between groups of 250 patients with COVID-19-positive cases and 250 healthy people. Following that, the images were examined using seven various machine learning approaches for recognition. These methods included naive Bayes, linear discriminant analysis, random forests, classification,k-nearest neighbors, and regression trees. The information from the photos was gathered using the Harr feature extraction method. The effectiveness of the algorithms was evaluated with the help of a variety of metrics, such asF1 score, precision,accuracy, recall, the area under the ROC curve, and the region of interest curve. According to our research, the Support Vector Machine algorithm had the highest accuracy, at 77%, while the Naive Bayes approach had the lowest accuracy, at 58%. By using machine learning and Harr feature extraction approaches, the Random Forest method yields the best results, based on our research. The development of future COVID-19 X-ray image-based automated diagnostic systems may be influenced by these findings. Results from the suggested model were comparable to those of cutting-edge models trained using transfer learning techniques. The proposed model's main advantage is that it has ten times fewer parameters than the most advanced models.A receiver operating characteristic (ROC) curve's F1 score, and the algorithms' accuracy, precision, the area under the curve, and recall were all used as metrics. According to our findings, the Naive Bayes method gained the least accuracy (58%) and the Support Vector Machine method produced the highest accuracy (77%) when used. Our results reveal that employing Harr feature extraction and machine learning techniques, the Random Forest strategy is the most successful way to recognize COVID-19 X-ray pictures. These findings may be pertinent to the development of automated COVID-19 diagnosis tools relying on X-ray images. The recommended model produced results that were competitive when measured against cutting-edge models trained using transfer learning techniques. The suggested model employs 10 times fewer parameters than the most advanced models, which is its key selling point.

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Section: International Journal On Recent and Innovation Trends In Com...mentioning

confidence: 99%

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

T.,

Jini

2023

IJRITCC

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Selective Kernel Networks for Lung Abnormality Diagnosis Using Chest X-rays

Phogat,

Parasu,

Prakash

et al. 2023

Lecture Notes in Networks and Systems

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Machine learning and deep learning covid-19 diagnosis system: key achievements, lessons learned, and a transfer learning algorithm

Salama,

Mohamed,

Abd-Ellah

2024

Soft Comput

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The COVID-19 pandemic is a novel, fast-spreading, deadly virus. It has spread around the world in an extremely short time. Due to its rapid spread and negative effects on all aspects of our lives (health, finances, stress, etc.), scientists are seeking to find accurate and fast solutions to this crisis. In our paper, we present a systematic literature review (SLR) of the different machine learning (ML) and deep learning (DL) techniques used for the detection, classification, and segmentation of COVID-19. We depend on our review of reliable databases such as IEEE Explore, Google Scholar, MDPI, Springer, PubMed, and Science Direct. By surveying approximately 978 papers, we found that 160 were more authorized, 77 of which were selected for review and met the criteria. A taxonomy is introduced to describe the sequence of our paper. Subsequently, a deep analysis and critical review of the academic literature were conducted to highlight the challenges and significant gaps identified in the introduced subject. The results revealed a shortage of research that assessed and established standards for the methods utilized for identifying and categorizing COVID-19 chest imaging techniques. As we continue the assessment and standardization process, three main difficulties are anticipated: the existence of various evaluation criteria for each task, the conflicts between these criteria, and the importance of these criteria. Moreover, we present a review of different systems used from the beginning of this crisis based on ML and DL by using different medical image modalities, such as chest X-ray, chest computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound imaging. We also highlight the datasets used and the different results of performance measures that have been developed by different researchers in this medical field. Finally, we discuss the limitations and lessons learned that are associated with the use of ML and DL techniques for diagnosing COVID-19. To support our work, we developed a new algorithm based on using transfer learning for several deep learning models and applied it to our own dataset. The aim of our paper is to collect various authorized data to help experts and specialists understand the importance of ML and DL systems in this respect, represent a new algorithm, and benefit them in future work toward fighting COVID-19.

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A novel COVID diagnosis and feature extraction based on discrete wavelet model and classification using X-ray and CT images

Cited by 7 publications

References 41 publications

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

A Comparative Study of HARR Feature Extraction and Machine Learning Algorithms for Covid-19 X-Ray Image Classification

Selective Kernel Networks for Lung Abnormality Diagnosis Using Chest X-rays

Machine learning and deep learning covid-19 diagnosis system: key achievements, lessons learned, and a transfer learning algorithm

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