Online Store - 8925533488 /89

Chennai - 8925533480 /81

Hyderabad - 8925533482 /83

Vijayawada -8925533484 /85

Covai - 8925533486 /87

Studies On Different CNN Algorithm for Face Skin Disease and Classification Of Clinical Images

( 0 Rating )
Shape Image One
0 student

Abstract:

Skin problems not only injure physical health but also induce psychological problems, especially for patients whose faces have been damaged or even disfigured. Using smart devices, most of the people are able to obtain convenient clinical images of their face skin condition. On the other hand, the convolutional neural networks (CNNs) have achieved near or even better performance than human beings in the imaging field. Therefore, this paper studied different CNN algorithms for face skin disease classification based on the clinical images. First, from Xiangya–Derm, which is, to the best of our knowledge, China’s largest clinical image dataset of skin diseases, we established a dataset that contains 2656 face images belonging to six common skin diseases [seborrheic keratosis (SK), actinic keratosis (AK), rosacea (ROS), lupus erythematosus (LE), basal cell carcinoma (BCC), and squamous cell carcinoma (SCC)]. We performed studies using five mainstream network algorithms to classify these diseases in the dataset and compared the results. Then, we performed studies using an independent dataset of the same disease types, but from other body parts, to perform transfer learning on our models. Comparing the performances, the models that used transfer learning achieved a higher average precision and recall for almost all structures. In the test dataset, which included 388 facial images, the best model achieved 92.9%, 89.2%, and 84.3% recalls for the LE, BCC, and SK, respectively, and the mean recall and precision reached 77.0% and 70.8%

 

 

Existing Method:

  • Principal Component Analysis
  • Local binary pattern and shape features
  • KNN and FNN classifier

 

Draw backs of Existing method:

  • High Computational load and poor discriminatory power.
  • LBP doesn’t differentiate the local texture region.
  • FNN is slow training for large feature set.
  • Less accuracy in classification

 

 

Proposed Method:

Skin lesion classification for Computer Aided Diagnosis (CAD) system based on,

  • CNN- Neural Network classifier

 

 

Block Diagram:

CNN algorithm for face skin disease and classification clinical images

 

 

Advantages:

  • CNN is fast and better compatible in classification.
  • Low computational complexity

 

 

Application:

  • Skin cancer diagnosis support system in Health care fields

 

 

Software Requirement:

  • Matlab
Curriculum is empty

pantech team

Agile Project Expert

Course Rating

0.00 average based on 0 ratings

Star
0%
Star
0%
Star
0%
Star
0%
Star
0%
Course Preview
  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

More Things You Might Like This

Free

Student Performance Prediction using Machine Learning

Abstract: Although the educational level of the Portuguese population has improved in the last decades, the statistics keep Portugal at Europe’s tail end due to its high student failure rates. In particular, lack of success in the core classes of Mathematics and the Portuguese language is extremely serious. On the other hand, the fields of

Free

Student feedback analysis

Abstract: Advances in natural language processing (NLP) and educational technology, as well as the availability of unprecedented amounts of educationally-relevant text and speech data, have led to an increasing interest in using NLP to address the needs of teachers and students. Educational applications differ in many ways, however, from the types of applications for which

Free

Machine Learning based Regression Model for Prediction of Soil Surface Humidity over Moderately Vegetated Fields

Abstract: Agriculture is one of the major revenue producing sectors of India and a source of survival. Numerous seasonal, economic and biological patterns influence the crop production but unpredictable changes in these patterns lead to a great loss to farmers. These risks can be reduced when suitable approaches are employed on data related to soil

Open Whatsapp Chat
Need Any Help?
Hello
Welcome to Pantech eLearning!..

How can i help you?