ArticleIEEE transactions on neural networks and learning systems2021
Anam-Net: Anamorphic Depth Embedding-Based Lightweight CNN for Segmentation of Anomalies in COVID-19 Chest CT Images.
Article in IEEE transactions on neural networks and learning systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 2 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 2 syntheses or guidelines pooled it, 179 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Pointer meters recognition method in the wild based on innovative deep learning techniques.Scientific reports · 2025Article
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- Identifying COVID-19-Infected Segments in Lung CT Scan Through Two Innovative Artificial Intelligence-Based Transformer Models.Archives of academic emergency medicine · 2025Article
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation.Sensors (Basel, Switzerland) · 2024Article
- Deep learning-based automatic pipeline system for predicting lateral cervical lymph node metastasis in patients with papillary thyroid carcinoma using computed tomography: A multi-center study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- An efficient segment anything model for the segmentation of medical images.Scientific reports · 2024Article
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- Advances in artificial intelligence in thyroid-associated ophthalmopathy.Frontiers in endocrinology · 2024Review
- COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.Frontiers in artificial intelligence · 2024Article
- An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases.Cell reports. Medicine · 2023Article
- Co-ERA-Net: Co-Supervision and Enhanced Region Attention for Accurate Segmentation in COVID-19 Chest Infection Images.Bioengineering (Basel, Switzerland) · 2023Article
- ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images.Biomedical signal processing and control · 2023Article
- Bio-medical imaging (X-ray, CT, ultrasound, ECG), genome sequences applications of deep neural network and machine learning in diagnosis, detection, classification, and segmentation of COVID-19: a Meta-analysis & systematic review.Multimedia tools and applications · 2023Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images.Sensors (Basel, Switzerland) · 2023Article
- A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme.Sensors (Basel, Switzerland) · 2023Review
- An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.Applied soft computing · 2023Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Chest computed tomography (CT) imaging has become indispensable for staging and managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies/abnormalities associated with COVID-19 has been performed majorly by the visual score. The development of automated methods for quantifying COVID-19 abnormalities in these CT images is invaluable to clinicians. The hallmark of COVID-19 in chest CT images is the presence of ground-glass opacities in the lung region, which are tedious to segment manually. We propose anamorphic depth embedding-based lightweight CNN, called Anam-Net, to segment anomalies in COVID-19 chest CT images. The proposed Anam-Net has 7.8 times fewer parameters compared to the state-of-the-art UNet (or its variants), making it lightweight capable of providing inferences in mobile or resource constraint (point-of-care) platforms. The results from chest CT images (test cases) across different experiments showed that the proposed method could provide good Dice similarity scores for abnormal and normal regions in the lung. We have benchmarked Anam-Net with other state-of-the-art architectures, such as ENet, LEDNet, UNet++, SegNet, Attention UNet, and DeepLabV3+. The proposed Anam-Net was also deployed on embedded systems, such as Raspberry Pi 4, NVIDIA Jetson Xavier, and mobile-based Android application (CovSeg) embedded with Anam-Net to demonstrate its suitability for point-of-care platforms. The generated codes, models, and the mobile application are available for enthusiastic users at https://github.com/NaveenPaluru/Segmentation-COVID-19.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.