Monkey Pox Disease Detecting System Using Deep Learning

Project Code :TCMAPY736


The main objective of the project is to detect the monkey pox disease on the skin using deep learning techniques.


Due of the recent monkey pox outbreak's quick spread to more than 40 nations outside of Africa, public health is now at risk. Monkey pox can be difficult to diagnose clinically in its early stages since it resembles both chickenpox and measles. Computer-assisted monkey pox lesion detection may be useful for surveillance and quick identification of suspected cases when confirmatory Polymerase Chain Reaction (PCR) assays are not easily accessible. Under the condition that there are enough training examples available, deep learning techniques have been demonstrated to be useful in the automated detection of skin lesions. Such databases are not, however, currently accessible for the monkey pox condition. In the present research first, we create the "Monkey pox Skin Lesion Dataset (MSLD)," which includes pictures of skin lesions caused by measles, chickenpox, and monkeypox. The majority of the photographs come from news websites, blogs, and publicly available case reports. A 3-fold cross-validation experiment is set up, and the sample size is increased through data augmentation. The second phase is categorising diseases like monkeypox using a number of pre-trained deep learning models, including VGG-16, CNN, and Mobile Net. 

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram


H/W Specifications:

Processor :  I5/Intel Processor

RAM  :  8GB (min)

Hard Disk :  128 GB

S/W Specifications:

Operating System :  Windows 10

Server-side Script :  Python 3.6

IDE : PyCharm, Jupyter notebook

Libraries Used:  Numpy, IO, OS, Flask, Keras, pandas, tensorflow

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