Comprehensive review of machine learning (ML) in image defogging: Taxonomy of concepts, scenes, feature extraction, and classification techniques
Images captured through a visual sensory system are degraded in a foggy scene, which
negatively influences recognition, tracking, and detection of targets. Efficient tools are …
negatively influences recognition, tracking, and detection of targets. Efficient tools are …
A survey of deep learning methods for multiple sclerosis identification using brain MRI images
M Sah, C Direkoglu - Neural Computing and Applications, 2022 - Springer
Multiple sclerosis (MS) is one of the most common inflammatory neurological diseases in
young adults. There are three types of MS:(1) In relapsing remitting MS (RRMS), people …
young adults. There are three types of MS:(1) In relapsing remitting MS (RRMS), people …
Electrocardiogram signal security by digital watermarking
Medical data is transferred between hospitals and healthcare providers via telemedicine to
improve patient care. This transfer exposes medical data to a number of security risks, and …
improve patient care. This transfer exposes medical data to a number of security risks, and …
A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images
Detecting brain tumours is complex due to the natural variation in their location, shape, and
intensity in images. While having accurate detection and segmentation of brain tumours …
intensity in images. While having accurate detection and segmentation of brain tumours …
A novel feature fusion based deep learning framework for white blood cell classification
N Dong, Q Feng, M Zhai, J Chang, X Mai - Journal of Ambient Intelligence …, 2023 - Springer
Traditional white blood cell detection usually requires artificial extraction of cell features,
which have higher resolution and contain more detailed information. However, due to the …
which have higher resolution and contain more detailed information. However, due to the …
Comparative Analysis of Different Deep Convolutional Neural Network Architectures for Classification of Brain Tumor on Magnetic Resonance Images
In the current study, the capability of pre-trained Deep Convolutional Neural Network
(DCNN) by ImageNet features is proposed for categorization of brain tumors by utilizing MR …
(DCNN) by ImageNet features is proposed for categorization of brain tumors by utilizing MR …
Multiclass classification of central nervous system brain tumor types based on proposed hybrid texture feature extraction methods and ensemble learning
KR Bhatele, SS Bhadauria - Multimedia Tools and Applications, 2023 - Springer
This paper presents an automated approach to perform multiclass classification of four
majorly diagnosed Central nervous system brain tumors. The Astrocytoma, Glioblastoma …
majorly diagnosed Central nervous system brain tumors. The Astrocytoma, Glioblastoma …
NeuroInsight: a revolutionary self-adaptive framework for precise brain tumor classification in medical imaging using adaptive deep learning
S Arora, GS Mishra - Signal, Image and Video Processing, 2025 - Springer
This paper presents a new framework for classifying brain tumours, using a self-adaptive
technique to improve image processing. The framework utilises a carefully selected dataset …
technique to improve image processing. The framework utilises a carefully selected dataset …
MEHW‐SVM multi‐kernel approach for improved brain tumour classification
G Dheepak, J Anita Christaline… - IET Image …, 2024 - Wiley Online Library
The human brain, the primary constituent of the nervous system, exhibits distinctive
complexities that present considerable difficulties for healthcare practitioners, specifically in …
complexities that present considerable difficulties for healthcare practitioners, specifically in …
Review on enhancing clinical decision support system using machine learning
A Masood, U Naseem, J Rashid, J Kim… - CAAI Transactions on …, 2024 - Wiley Online Library
Clinical decision‐making is a complex patient‐centred process. For an informed clinical
decision, the input data is very thorough ranging from detailed family history, environmental …
decision, the input data is very thorough ranging from detailed family history, environmental …