ArticleComputers in biology and medicine2023
Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.
Article in Computers in biology and medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Unlocking the Power of 3D Convolutional Neural Networks for COVID-19 Detection: A Comprehensive Review.Journal of imaging informatics in medicine · 2025Review
- Evaluating the environmental sustainability of AI in radiology: a systematic review of current practice.BMJ digital health & AI · 2025Article
- FACNN: fuzzy-based adaptive convolution neural network for classifying COVID-19 in noisy CXR images.Medical & biological engineering & computing · 2024Article
- Improved Resistive Switching Characteristics and Synaptic Functions of InZnO/SiOMaterials (Basel, Switzerland) · 2023Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- SuperMini-seg: An ultra lightweight network for COVID-19 lung infection segmentation from CT images.Biomedical signal processing and control · 2023Article
- Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.Expert systems with applications · 2023Article
- Comparing machine learning algorithms to predict COVID‑19 mortality using a dataset including chest computed tomography severity score data.Scientific reports · 2023Article
- SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full Blood Examination.TheScientificWorldJournal · 2023Article
- An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Development and validation of a machine learning model for differential diagnosis of malignant pleural effusion using routine laboratory data.Therapeutic advances in respiratory diseaseObservational
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Abstract
In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.
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