Imaging based cervical cancer diagnostics using small object detection - generative adversarial networks

Development of an End-to-End Deep Learning Framework for Sign Language Recognition, Translation, and Video Generation

“…The emergence of deep learning techniques entered all the fields to exhibit their strength towards robust model development. The deep learning techniques produces impressive results in areas such as agriculture [1], anomaly detection [2], activity recognition [3], business analysis [4,5], crop selection [6], defect monitoring [7], DNA systems [8], earth analysis [9], fraud detection [10], genomic prediction [11], human activity recognition [12], image classification [13], job matching [14], kinematic analysis [15], location prediction [16], medical systems [17,18,19,20], network traffic analysis [21], number plate recognition [22], object detection [23], predictive maintenance [24], quality control [25], robotics [26], stock prediction [27], time series data analysis [28], and text generation [29], unmanned vehicle path findings [30], vehicle monitoring [31], weather forecasting [32], x-ray imaging [33], YouTube video analysis [34],zone segmentation [35]. These developments highly motivate us to pursue research in the deep learning area.…”

Dynamic GAN for high-quality sign language video generation from skeletal poses using generative adversarial networks

“…Sign language greatly improves the communication skills of the deaf-mute community as well as explores the needs and emotions of such people. Sign languages are highly structured, visual conveying, and multi-channel based one, expressed via gestures and utilizes human body parts such as hands, face, eyes and gaze movements [42]. These components are usually termed as manual components for hands actions and non-manual components for facial and mouth expressions [8].…”

“…The quality of generated results does not comply with the expectations. The recent advancements in generative adversarial networks have been attained wider attention among researchers for developing various applications like synthesizing medical images [25,42], text-to-image translation [17], video analytics [26], and creating human images that do not exist in the world [12]. This powerfulness of the GAN models directs the researchers to develop efficient models to generate high-quality images or videos.…”

Special issue on Sentient Multimedia Systems

“…The set of papers on Intelligent Multimedia Systems include two papers on Action Recognition [3,6], and four papers on Machine Learning Architectures and Models for Multimedia Applications [1,2,8,9].…”

“…The category of Machine Learning Architectures and Models for Multimedia Applications includes a paper on the automatic diagnosis of cervical cancer [9], one on a machine learning framework to increase safety at work [2], one on the classification of user transportation modalities [1], and finally, one on the prediction of Cyber-Attacks [8]. The paper by Elakkiya R et al proposes a hybrid deep learning algorithm for cervical localization and precancerous/cancerous lesion detection, in order to provide an end-to-end application for the early diagnosis and prognosis of cervical cancer, which represents one of the curable cancers when it is diagnosed in the early stages [9]. The algorithm accurately spots the cervix without manual annotations and interventions, classifying the cervical cells as normal, precancerous, and cancerous lesions, also identifying the type and stage of cervical cancer.…”