Multiple Class Breast Cancer Detection Method Based on Deep Learning and MIRRCNN Model-Reference-Cited by-同舟云学术

Multiple Class Breast Cancer Detection Method Based on Deep Learning and MIRRCNN Model

Published:2022-07-20 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Inventive Computation Technologies (ICICT)
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Author:

Sajiv G¹,Ramkumar G¹

Affiliation:

1. Saveetha School of Engineering, SIMATS,Chennai

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9850395/9850438/09850707.pdf?arnumber=9850707

Reference23 articles.

1. Road Accident Data Analytics Using Map - Reduce Concept;joshua samuel raj;International Journal of Innovative Technology and Exploring Engineering,2019

2. Driver Sleepiness Detection Using Deep Learning Convolution Neural Network Classifier

3. Intelligent Drowsiness and Illness Detection Assist System for Drivers

4. An ML Approach for Household Power Consumption

5. An Ads-Csab Approach for Economic Denial of Sustainability Attacks in Cloud Storage;karthika;International Journal of Scientific & Technology Research,2020

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