scholarly journals Arrhythmia and Disease Classification Based on Deep Learning Techniques

2022 ◽  
Vol 31 (2) ◽  
pp. 835-851
Ramya G. Franklin ◽  
B. Muthukumar
2021 ◽  
Vol 11 (2) ◽  
pp. 48-52
N.Vanitha ◽  

Dermatological disorders are one among the foremost widespread diseases within the world. Despite being common its diagnosis is extremely difficult due to its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision-based techniques (deep learning) to automatically predict the varied sorts of skin diseases. The system makes use of deep learning technology to coach itself with the varied skin images. the most objective of this technique is to realize maximum accuracy of disease of the skin prediction. The people health quite the other diseases. Skin diseases are mostly caused by mycosis, bacteria, allergy, or viruses, etc. The lasers advancement and Photonics based medical technology is employed in diagnosis of the skin diseases quickly and accurately. The medical equipment for such diagnosis is restricted and costliest. So, Deep learning techniques helps in detection of disease of the skin at an initial stage. The feature extraction plays a key role in classification of skin diseases. The usage of Deep Learning algorithms has reduced the necessity for human labor, like manual feature extraction and data reconstruction for classification purpose.

2021 ◽  
Aniket Dhole ◽  
Mohit Gandhi ◽  
Shrishail Kumbhar ◽  
Harsh Singhal ◽  
Sonal Gore

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Sign in / Sign up

Export Citation Format

Share Document