Multiple Type of Blood Cancer Detection System Using CNN

EasyChair Preprint 9810

Abstract

Now a day’s blood cancer is one of the most common causes of death with an incidence of more than one lakh people being diagnosed every year .The detection of multiple types blood cancer still remains a significant challenge in healthcare.Various applications are developed which detect blood cancer  from the input of provided by the user through images.It detect that whether the cell is infected with cancer or not but system is not able to detect different types of cancer properly.Therefore ,it is very difficult to understand which type of blood cancer detected.Developed system are not able to find the type of blood cancer.To overcome these challenging problems,we have proposed a multiple type blood cancer detection system ,which can detect a type of blood cancer through the input provided by the user  in the form of image using Convolution Neural Network (CNN) model.Also it improves the accuracy of the model. In the form of output the system provides the user the information about the particular type of blood cancer and also generates the report of  the overall result.The proposed application will help user to identify the type of blood cancer and also provide information and precautions to be taken about a particular type of blood cancer. 

Keyphrases: Convolution Neural Networks, Leukemia, Lymphoma, Multiple Myeloma, classification algorithms, deep learning, image processing

@booklet{EasyChair:9810,
  author    = {Sushmita Jagtap and Shamla Mantri},
  title     = {Multiple Type of Blood Cancer Detection System Using CNN},
  howpublished = {EasyChair Preprint 9810},
  year      = {EasyChair, 2023}}