The Architecture of Densely Connected Convolution Neural Network ==>
Size of Input Image at different Layer ==>
Summary ==>
i)The model accepts the histological reports of the user as input.
ii)Run the trained model on the data provided.
iii)Computation time may vary from seconds to a few minutes.
iv)Output is the status and class of cancer present in the tissue of the image provided.
Data set is aviable Here.
Technology ==>
Deep neural network algorithms such as ConvNets, DenseNets, etc. are used in the project.The entire development has been done in Python.
Aim ==>
The project aims to revolutionize cancer treatment throughout the globe and to remove the ambiguity indecision making. This product can act as a confirmatory step which can be used by patients, doctors as well as medical institutions.This can drastically decrease the rate of deaths happening across the globe due to the deadly disease by making the system more efficient and transparent.