FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Namburu, Anupama; Sumathi, D.; Raut, Roshani; Jhaveri, Rutvij H.; Dhanaraj, Rajesh Kumar; Subbulakshmi, N.; Balusamy, Balamurugan

doi:10.1155/2022/2110785

Cited by 28 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this architecture only focuses on direct convolution and other types of software optimizations for reducing memory size can hamper its performance. A high-level synthesis (HLS) based approach for designing the architecture was adopted by A. Namburu et al 16 that accelerated CNN operation for detecting the patients with COVID-19 based on their chest X-Ray. Another HLS based approach was implemented by H. Zairi, et al 17 for accelerating neural network for arrythmia classifications.…”

Section: Related Workmentioning

confidence: 99%

FPGA based CNN accelerator for high-speed biomedical application

Nehete

Verma

Gupta

et al. 2023

High-Speed Biomedical Imaging and Spectroscopy VIII

View full text Add to dashboard Cite

The rise in visual dataset generation has necessitated the recent advancements in the field of Deep Neural Networks (DNNs). Application domains like biomedical imaging require a high level of precision which is suitably achieved using convolutional neural networks (CNNs) at the expense of increased computation, hardware, and power resources. The implementation of such CNN architectures is constrained by currently available resource limited embedded and application-specific integrated circuit (ASIC) systems. In this work, a field-programmable gate array (FPGA) based hardware accelerator having a generalized architecture for convolution and fully connected (FC) layers has been presented that exploits a massive level of intra-layer parallelism. Compute intensive convolution layers are replaced by depthwise separable (DS) convolution layers that reduced the number of computations and memory access by 7.8x and 10x respectively for VGG8 network after detailed design space exploration. Furthermore, parallel computation of arithmetic tasks reduced the compute bound for the proposed architecture. Reduced precision data type for both input and weights resulted in overall reduction in latency and resource utilization. FPGA implementation results of the proposed CNN accelerator for classifiers trained on subsets of MedMNIST dataset depict a balance between high performance of 214.5 GOP/s for DS convolution layer and low resource utilization.

show abstract

Section: Related Workmentioning

confidence: 99%

FPGA based CNN accelerator for high-speed biomedical application

Nehete

Verma

Gupta

et al. 2023

High-Speed Biomedical Imaging and Spectroscopy VIII

View full text Add to dashboard Cite

show abstract

“…Machine learning has been used for various healthcare applications such as tumor detection, activity recognition, health assessment, dementia detection, and many others [28][29][30][31][32][33]. Authors in this research [34] verify a machine learning model developed on an open-source dataset and then optimize it for chest X-rays of patients with significant pneumothorax.…”

Section: Machine and Deep Learning Techniquesmentioning

confidence: 99%

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

Almadhor¹,

Khan²,

Wechtaisong³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Pneumothorax is a thoracic condition that occurs when a person's lungs collapse, causing air to enter the pleural cavity, the area close to the lungs and chest wall. The most persistent disease, as well as one that necessitates particular patient care and the privacy of their health records. The radiologists find it challenging to diagnose pneumothorax due to the variations in images. Deep learning-based techniques are commonly employed to solve image categorization and segmentation problems. However, it is challenging to employ it in the medical field due to privacy issues and a lack of data. To address this issue, a federated learning framework based on an Xception neural network model is proposed in this research. The pneumothorax medical image dataset is obtained from the Kaggle repository. Data preprocessing is performed on the used dataset to convert unstructured data into structured information to improve the model's performance. Min-max normalization technique is used to normalize the data, and the features are extracted from chest Xray images. Then dataset converts into two windows to make two clients for local model training. Xception neural network model is trained on the dataset individually and aggregates model updates from two clients on the server side. To decrease the over-fitting effect, every client analyses the results three times. Client 1 performed better in round 2 with a 79.0% accuracy, and client 2 performed better in round 2 with a 77.0% accuracy. The experimental result shows the effectiveness of the federated learning-based technique on a deep neural network, reaching a 79.28% accuracy while also providing privacy to the patient's data.

show abstract

“…A common method for predicting sets is to employ the majority voting strategy, which assumes that at least half of the instances in a collection reflect the category to which the set belongs [ 6 ]. Voting thresholds were used to grade hepatocellular cancer tumors in [ 7 ]. If want to get the best results, voting thresholds for each class need to be predefined on the basis of on experience in each topic.…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Machine Learning‐Based Optimization Technique to Predict Lung Cancer Patient Survival

Kukreja

Sabharwal

Shah

et al. 2023

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Cancer has been a significant threat to human health and well-being, posing the biggest obstacle in the history of human sickness. The high death rate in cancer patients is primarily due to the complexity of the disease and the wide range of clinical outcomes. Increasing the accuracy of the prediction is equally crucial as predicting the survival rate of cancer patients, which has become a key issue of cancer research. Many models have been suggested at the moment. However, most of them simply use single genetic data or clinical data to construct prediction models for cancer survival. There is a lot of emphasis in present survival studies on determining whether or not a patient will survive five years. The personal issue of how long a lung cancer patient will survive remains unanswered. The proposed technique Naive Bayes and SSA is estimating the overall survival time with lung cancer. Two machine learning challenges are derived from a single customized query. To begin with, determining whether a patient will survive for more than five years is a simple binary question. The second step is to develop a five-year survival model using regression analysis. When asked to forecast how long a lung cancer patient would survive within five years, the mean absolute error (MAE) of this technique’s predictions is accurate within a month. Several biomarker genes have been associated with lung cancers. The accuracy, recall, and precision achieved from this algorithm are 98.78%, 98.4%, and 98.6%, respectively.

show abstract

FPGA-Based Deep Learning Models for Analysing Corona Using Chest X-Ray Images

Cited by 28 publications

References 28 publications

FPGA based CNN accelerator for high-speed biomedical application

FPGA based CNN accelerator for high-speed biomedical application

Chest Radiographs Based Pneumothorax Detection Using Federated Learning

A Heuristic Machine Learning‐Based Optimization Technique to Predict Lung Cancer Patient Survival

Contact Info

Product

Resources

About