scholarly journals Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences

2020 ◽  
Vol 32 (3) ◽  
pp. 588-601 ◽  
Author(s):  
Marcel Trotzek ◽  
Sven Koitka ◽  
Christoph M. Friedrich
2021 ◽  
Author(s):  
Rita Zgheib ◽  
Ghazar Chahbandarian ◽  
Firuz Kamalov ◽  
Osman El Labban

Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.


Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 118684
Author(s):  
Hakima Cherif ◽  
Abdelhamid Benakcha ◽  
Ismail Laib ◽  
Seif Eddine Chehaidia ◽  
Arezky Menacer ◽  
...  

2020 ◽  
Vol 8 (6) ◽  
pp. 3929-3933

Dermatology is a medical field that treats skin health and diseases. People feeling disease symptoms of an affecting the skin must consult a dermatologist if this stipulation does not respond to home remedy. Early detection and treatment can correct most skin disorders. Basal Cell Carcinoma (BCC), Melanoma and Squamous Cell Carcinoma (SCC) are typically appearing type of skin cancers. The purpose of this effort is to provide a system that can be deployed to classify dermatoscopic images to predict skin diseases with early detection and higher accuracy . This work is a concrete effort to accomplish higher degree of accuracy for clinical usage by implementing advances in soft computing and image processing like deep learning and in-depth neural networks in an early stage for 7 class classification for HAM10000 dataset.


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