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Plant Disease Recognition Using Machine Learning and Deep Learning Classifiers Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Colon Cancer Nuclei Classification with Convolutional Neural Networks

Authors : Kancharagunta Kishan Babu, Bhavanam Santhosh Reddy, Akhil Chimma, Paruchuri Pranav, Kamatam Santhosh Kumar

Published in: Advanced Computing

Publisher: Springer Nature Switzerland

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Abstract

CRC or Colorectal Cancer also called as bowel cancer is the development of cancer from colon or rectum. Detection of CRC can be very essential that can help the diagnosed with effectual treatment preventing potential loss of life. Conventional methods can be challenging because of its excessive dependence on the expert to detect accurately. This paper aims to compare results obtained from popular deep learning models such as AlexNet, GoogleNet, MobileNet, by performing on the “CRCHistoPhenotypes” dataset furthermore, inter-comparison of the same is done by applying Data Augmentation methods. Comparison is done on the basis of training time, accuracy, weighted f1 score, specificity and sensitivity. An enhancement in testing accuracy was observed, even in the case of the state-of-the-art network, GoogLeNet. It exhibited an increase of around 2.3%, achieving an impressive 80% accuracy following the utilization of data augmentation methods.

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previous chapter Innovation in the Field of Oncology: Early Lung Cancer Detection and Classification Using AI
next chapter Genetic Algorithm-Based Optimization of UNet for Breast Cancer Classification: A Lightweight and Efficient Approach for IoT Devices
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Metadata
Title
Colon Cancer Nuclei Classification with Convolutional Neural Networks
Authors
Kancharagunta Kishan Babu
Bhavanam Santhosh Reddy
Akhil Chimma
Paruchuri Pranav
Kamatam Santhosh Kumar
Copyright Year
2024
Book
Advanced Computing

Print ISBN: 978-3-031-56702-5

Electronic ISBN: 978-3-031-56703-2

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-56703-2_30

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