Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion
Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it
has gradually become the leading approach in many fields. It is currently playing a vital role …
has gradually become the leading approach in many fields. It is currently playing a vital role …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Towards a better understanding of transfer learning for medical imaging: a case study
Featured Application The proposed intelligent medical system is applicable for a medical
diagnostic system, especially for the diagnosis of diabetic foot ulcer. Abstract One of the …
diagnostic system, especially for the diagnosis of diabetic foot ulcer. Abstract One of the …
Novel transfer learning approach for medical imaging with limited labeled data
Deep learning requires a large amount of data to perform well. However, the field of medical
image analysis suffers from a lack of sufficient data for training deep learning models …
image analysis suffers from a lack of sufficient data for training deep learning models …
Recent trends in smartphone-based detection for biomedical applications: a review
Smartphone-based imaging devices (SIDs) have shown to be versatile and have a wide
range of biomedical applications. With the increasing demand for high-quality medical …
range of biomedical applications. With the increasing demand for high-quality medical …
Deep learning models for classification of red blood cells in microscopy images to aid in sickle cell anemia diagnosis
Sickle cell anemia, which is also called sickle cell disease (SCD), is a hematological
disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death …
disorder that causes occlusion in blood vessels, leading to hurtful episodes and even death …
Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model
Breast cancer is a significant factor in female mortality. An early cancer diagnosis leads to a
reduction in the breast cancer death rate. With the help of a computer-aided diagnosis …
reduction in the breast cancer death rate. With the help of a computer-aided diagnosis …
Robust Classification and Detection of Big Medical Data Using Advanced Parallel K-Means Clustering, YOLOv4, and Logistic Regression
Big-medical-data classification and image detection are crucial tasks in the field of
healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring …
healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring …
Multiclass wound image classification using an ensemble deep CNN-based classifier
Acute and chronic wounds are a challenge to healthcare systems around the world and
affect many people's lives annually. Wound classification is a key step in wound diagnosis …
affect many people's lives annually. Wound classification is a key step in wound diagnosis …