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Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. ...
Employing machine learning techniques in monitoring autocorrelated profiles
(
Springer Science and Business Media Deutschland GmbH
, 2023 , Article)
In profile monitoring, it is usually assumed that the observations between or within each profile are independent of each other. However, this assumption is often violated in manufacturing practice, and it is of utmost ...
Using artificial intelligence to improve body iron quantification: A scoping review
(
Elsevier
, 2023 , Article Review)
This scoping review explores the potential of artificial intelligence (AI) in enhancing the screening, diagnosis, and monitoring of disorders related to body iron levels. A systematic search was performed to identify studies ...
Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks
(
MDPI
, 2021 , Article)
Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming ...
PredictPTB: an interpretable preterm birth prediction model using attention-based recurrent neural networks
(
BioMed Central Ltd
, 2022 , Article)
Background: Early identification of pregnant women at risk for preterm birth (PTB), a major cause of infant mortality and morbidity, has a significant potential to improve prenatal care. However, we lack effective predictive ...
BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
(
Springer Science and Business Media Deutschland GmbH
, 2023 , Article)
Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge ...
An active learning method for diabetic retinopathy classification with uncertainty quantification
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Article)
In recent years, deep learning (DL) techniques have provided state-of-the-art performance in medical imaging. However, good quality (annotated) medical data is in general hard to find due to the usually high cost of medical ...
Smartphone-based diabetic retinopathy severity classification using convolution neural networks
(
Springer
, 2021 , Conference Paper)
With diabetes growing at an alarming rate, changes in the retina causes a condition called diabetic retinopathy which eventually leads to blindness. Early detection of diabetic retinopathy is the best way to provide good ...
Edge Detection with multi-scale representation and refined Network
(
Institution of Engineering and Technology
, 2022 , Conference Paper)
Edge detection is a representation of boundaries between objects and regions in an image. Due to the variations of types, scales, intensities as well as background, the detection of these boundaries represents a challenge ...
Deep Learning-Based Conjunctival Melanoma Detection Using Ocular Surface Images
(
springer link
, 2023 , Article)
The human eye could be affected with conjunctival melanoma, which indicates a fatal malignant growth of the eye. Being a very rare disease, there exists a lack of related data in the literature. Also, very few studies ...