[HTML][HTML] A comprehensive survey of deep learning in the field of medical imaging and medical natural language processing: Challenges and research directions
B Pandey, DK Pandey, BP Mishra… - Journal of King Saud …, 2022 - Elsevier
The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
in health. Deep learning is a highly advanced successor of artificial neural networks, having …
[HTML][HTML] Impact of quality, type and volume of data used by deep learning models in the analysis of medical images
The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …
IMNets: Deep learning using an incremental modular network synthesis approach for medical imaging applications
Deep learning approaches play a crucial role in computer-aided diagnosis systems to
support clinical decision-making. However, develo** such automated solutions is …
support clinical decision-making. However, develo** such automated solutions is …
Analyzing malaria disease using effective deep learning approach
K Sriporn, CF Tsai, CE Tsai, P Wang - Diagnostics, 2020 - mdpi.com
Medical tools used to bolster decision-making by medical specialists who offer malaria
treatment include image processing equipment and a computer-aided diagnostic system …
treatment include image processing equipment and a computer-aided diagnostic system …
Empirical analysis of a fine-tuned deep convolutional model in classifying and detecting malaria parasites from blood smears
In this work, we empirically evaluated the efficiency of the recent EfficientNetB0 model to
identify and diagnose malaria parasite infections in blood smears. The dataset used was …
identify and diagnose malaria parasite infections in blood smears. The dataset used was …
AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images
Malaria is a significant public health concern, with∼ 95% of cases occurring in Africa, but
accurate and timely diagnosis is problematic in remote and low-income areas. Here, we …
accurate and timely diagnosis is problematic in remote and low-income areas. Here, we …
Dlrfnet: deep learning with random forest network for classification and detection of malaria parasite in blood smear
In healthcare, observing the features and areas of malaria in microscopic images is crucial
for the diagnosis and treatment of plasmodium malaria parasites for automated detection …
for the diagnosis and treatment of plasmodium malaria parasites for automated detection …
Assessment of deep learning models for cutaneous Leishmania parasite diagnosis using microscopic images
Cutaneous leishmaniasis (CL) is a common illness that causes skin lesions, principally
ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) …
ulcerations, on exposed regions of the body. Although neglected tropical diseases (NTDs) …
[HTML][HTML] Febrile disease modeling and diagnosis system for optimizing medical decisions in resource-scarce settings
Febrile diseases are highly prevalent in tropical regions due to elevated humidity and high
temperatures. These regions, mainly comprising low-and middle-income countries, often …
temperatures. These regions, mainly comprising low-and middle-income countries, often …
[PDF][PDF] Malaria parasite detection on microscopic blood smear images with integrated deep learning algorithms.
CB Jones, C Murugamani - Int. Arab J. Inf. Technol., 2023 - ccis2k.org
Malaria is a deadly syndrome formed by the Plasmodium parasite that spreads through the
bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is …
bite of infected Anopheles mosquitoes. There are several drugs to cure malaria but it is …