Facial emotion recognition using conventional machine learning and deep learning methods: current achievements, analysis and remaining challenges
AR Khan - Information, 2022 - mdpi.com
Facial emotion recognition (FER) is an emerging and significant research area in the pattern
recognition domain. In daily life, the role of non-verbal communication is significant, and in …
recognition domain. In daily life, the role of non-verbal communication is significant, and in …
Deep learning for image enhancement and correction in magnetic resonance imaging—state-of-the-art and challenges
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical
diagnoses and research which underpin many recent breakthroughs in medicine and …
diagnoses and research which underpin many recent breakthroughs in medicine and …
Brain tumor detection and multi‐classification using advanced deep learning techniques
A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at
an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and …
an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and …
Computer vision for microscopic skin cancer diagnosis using handcrafted and non‐handcrafted features
T Saba - Microscopy Research and Technique, 2021 - Wiley Online Library
Skin covers the entire body and is the largest organ. Skin cancer is one of the most dreadful
cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the …
cancers that is primarily triggered by sensitivity to ultraviolet rays from the sun. However, the …
Artificial intelligence in brain tumor detection through MRI scans: advancements and challenges
A brain tumor is one of the most perilous diseases in human beings. The manual
segmentation of brain tumors is costly and takes a lot of time; due to this reason, automated …
segmentation of brain tumors is costly and takes a lot of time; due to this reason, automated …
A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges
In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
UFace: An Unsupervised Deep Learning Face Verification System
Deep convolutional neural networks are often used for image verification but require large
amounts of labeled training data, which are not always available. To address this problem …
amounts of labeled training data, which are not always available. To address this problem …
Speechin: A smart necklace for silent speech recognition
This paper presents SpeeChin, a smart necklace that can recognize 54 English and 44
Chinese silent speech commands. A customized infrared (IR) imaging system is mounted on …
Chinese silent speech commands. A customized infrared (IR) imaging system is mounted on …
Transformer models for enhancing AttnGAN based text to image generation
S Naveen, MSSR Kiran, M Indupriya… - Image and Vision …, 2021 - Elsevier
Deep neural networks are capable of producing photographic images that depict given
natural language text descriptions. Such models have huge potential in applications such as …
natural language text descriptions. Such models have huge potential in applications such as …
Improving satellite image classification accuracy using GAN-based data augmentation and vision transformers
Deep learning (DL) algorithms have shown great potential in classifying satellite imagery but
require large amounts of labeled data to make accurate predictions. However, generating …
require large amounts of labeled data to make accurate predictions. However, generating …