[HTML][HTML] Image analysis and machine learning for detecting malaria
Malaria remains a major burden on global health, with roughly 200 million cases worldwide
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
and more than 400,000 deaths per year. Besides biomedical research and political efforts …
Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images
Chest X-ray images are used in deep convolutional neural networks for the detection of
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
COVID-19, the greatest human challenge of the 21st century. Robustness to noise and …
An efficient model of residual based convolutional neural network with Bayesian optimization for the classification of malarial cell images
A Diker - Computers in Biology and Medicine, 2022 - Elsevier
Background Malaria is a disease caused by the Plasmodium parasite, which results in
millions of deaths in the human population worldwide each year. It is therefore considered a …
millions of deaths in the human population worldwide each year. It is therefore considered a …
An Automatic Malaria Disease Diagnosis Framework Integrating Blockchain Enabled Cloud-edge Computing and Deep Learning
S Chen, S Zhao, C Huang - IEEE Internet of Things Journal, 2023 - ieeexplore.ieee.org
Malaria is a life-threatening disease, which mainly occurs in develo** countries and
regions with poor sanitary conditions. Early diagnosis of malaria will effectively decrease the …
regions with poor sanitary conditions. Early diagnosis of malaria will effectively decrease the …
Optimal policy learning for disease prevention using reinforcement learning
Diseases can have a huge impact on the quality of life of the human population. Humans
have always been in the quest to find strategies to avoid diseases that are life‐threatening or …
have always been in the quest to find strategies to avoid diseases that are life‐threatening or …
Deep learning-assisted medical image compression challenges and opportunities: systematic review
Over the preceding decade, there has been a discernible surge in the prominence of
artificial intelligence, marked by the development of various methodologies, among which …
artificial intelligence, marked by the development of various methodologies, among which …
Towards an adversarially robust convolutional neural network for automated diagnosis of malaria infection from microscopy images
Malaria is a life-threatening mosquito-borne disease of global importance, and it is most
prevalent in the low-income countries of the develo** world. The diagnosis of malaria …
prevalent in the low-income countries of the develo** world. The diagnosis of malaria …
Malaria disease prediction based on machine learning
O Iradukunda, H Che, J Uwineza… - … conference on signal …, 2019 - ieeexplore.ieee.org
Malaria detection is a stressful job for most doctors and it requires experiences and
expertise. The machine learing (ML) method can be used to releave this issue. This paper …
expertise. The machine learing (ML) method can be used to releave this issue. This paper …
Malaria detection using image processing and machine learning
PK Maduri, S Agrawal, A Rai… - 2021 3rd International …, 2021 - ieeexplore.ieee.org
Malaria which has now become a common human disease is diagnosed in the present
scenario starting with a clinical screening and then by medical treatment. Automated …
scenario starting with a clinical screening and then by medical treatment. Automated …
YOLO-PAM: Parasite-Attention-Based Model for Efficient Malaria Detection
Malaria is a potentially fatal infectious disease caused by the Plasmodium parasite. The
mortality rate can be significantly reduced if the condition is diagnosed and treated early …
mortality rate can be significantly reduced if the condition is diagnosed and treated early …