A systematic literature review of machine learning application in COVID-19 medical image classification
TW Cenggoro, B Pardamean - Procedia computer science, 2023 - Elsevier
Detecting COVID-19 as early as possible and quickly is one way to stop the spread of
COVID-19. Machine learning development can help to diagnose COVID-19 more quickly …
COVID-19. Machine learning development can help to diagnose COVID-19 more quickly …
A comprehensive survey of deep learning research on medical image analysis with focus on transfer learning
This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
gaps, constraints, and limitations in the field to provide an overview of current solutions used …
An optimized deep learning architecture for breast cancer diagnosis based on improved marine predators algorithm
EH Houssein, MM Emam, AA Ali - Neural Computing and Applications, 2022 - Springer
Breast cancer is the second leading cause of death in women; therefore, effective early
detection of this cancer can reduce its mortality rate. Breast cancer detection and …
detection of this cancer can reduce its mortality rate. Breast cancer detection and …
[HTML][HTML] Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning
Lung malignancy is one of the most common causes of death in the world caused by
malignant lung nodules which commonly diagnosed radiologically by radiologists …
malignant lung nodules which commonly diagnosed radiologically by radiologists …
How transferable are self-supervised features in medical image classification tasks?
T Truong, S Mohammadi… - Machine Learning for …, 2021 - proceedings.mlr.press
Transfer learning has become a standard practice to mitigate the lack of labeled data in
medical classification tasks. Whereas finetuning a downstream task using supervised …
medical classification tasks. Whereas finetuning a downstream task using supervised …
Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning
Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR
will cause patients to experience blindness slowly. Generally, DR can be detected by using …
will cause patients to experience blindness slowly. Generally, DR can be detected by using …
Automated detection of pneumoconiosis with multilevel deep features learned from chest X-Ray radiographs
Early detection of pneumoconiosis in X-Rays has been a challenging task that leads to high
inter-and intra-reader variability. Motivated by the success of deep learning in general and …
inter-and intra-reader variability. Motivated by the success of deep learning in general and …
Transfer learning using inception-ResNet-v2 model to the augmented neuroimages data for autism spectrum disorder classification
From a psychiatric perspective, the detection of Autism Spectrum Disorders (ASD) can be
seen from the differences in some parts of the brain. The availability of the four-dimensional …
seen from the differences in some parts of the brain. The availability of the four-dimensional …
A design of deep learning experimentation for fruit freshness detection
F Valentino, TW Cenggoro… - IOP conference series …, 2021 - iopscience.iop.org
Indonesia is a country with a tropical climate so that fruit and vegetable plants can grow
easily in Indonesia. Fruits have many good nutrients such as vitamins, proteins and others …
easily in Indonesia. Fruits have many good nutrients such as vitamins, proteins and others …
Deep learning as a vector embedding model for customer churn
TW Cenggoro, RA Wirastari, E Rudianto… - Procedia Computer …, 2021 - Elsevier
To face the tight competition in the telecommunication industry, it is important to minimize the
rate of customers stopping their service subscription, which is known as customer churn. For …
rate of customers stopping their service subscription, which is known as customer churn. For …