Detecting Bone Tumor on Applying Edge Computational Deep Learning Approach

EasyChair Preprint no. 9627

Abstract

Bone cancer affects the majority of the elderly in today's world. It directly affects the neurotransmitters and leads to dementia. MRI images can spot bone irregularities related to mild cognitive damage. It can be useful for predicting bone cancer, though it is a big challenge. In this research, a novel technique is proposed to detect Bone cancer using Adaboost classifier with a hybrid ACO algorithm. Initially, MRI image features are extracted and the best features are identified by the Adaboost curvelet transform classifier. The proposed methods yield greater accuracy than the existing systems for analyzing MRI images and give excellent classification accuracy. Three metrics namely accuracy, specificity, and sensitivity are used to evaluate the proposed method. Based on the results the proposed methods yield greater accuracy than the existing systems.

Keyphrases: Adaboost classifier, Ant Colony Optimization, Bone cancer, Convolutional Neural Network, Curvelet transform

@Booklet{EasyChair:9627,
  author = {G Megala and P Swarnalatha and R Venkatesan},
  title = {Detecting Bone Tumor on Applying Edge Computational Deep Learning Approach},
  howpublished = {EasyChair Preprint no. 9627},

  year = {EasyChair, 2023}}