Deep Learning Predictive Model for Colon Cancer Patient using CNN-based Classification

Zarrin Tasnim, Sovon Chakraborty, F. M.Javed Mehedi Shamrat, Ali Newaz Chowdhury, Humaira Alam Nuha, Asif Karim, Sabrina Binte Zahir, Md Masum Billah

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Abstract

In recent years, the area of Medicine and Healthcare has made significant advances with the assistance of computational technology. During this time, new diagnostic techniques were developed. Cancer is the world's second-largest cause of mortality, claiming the lives of one out of every six individuals. The colon cancer variation is the most frequent and lethal of the numerous kinds of cancer. Identifying the illness at an early stage, on the other hand, substantially increases the odds of survival. A cancer diagnosis may be automated by using the power of Artificial Intelligence (AI), allowing us to evaluate more cases in less time and at a lower cost. In this research, CNN models are employed to analyse imaging data of colon cells. For colon cell image classification, CNN with max pooling and average pooling layers and MobileNetV2 models are utilized. To determine the learning rate, the models are trained and evaluated at various Epochs. It's found that the accuracy of the max pooling and average pooling layers is 97.49% and 95.48%, respectively. And MobileNetV2 outperforms the other two models with the most remarkable accuracy of 99.67% with a data loss rate of 1.24.

Original languageEnglish
Pages (from-to)687-696
Number of pages10
JournalInternational Journal of Advanced Computer Science and Applications
Volume12
Issue number8
DOIs
Publication statusPublished - Jul 2021
Externally publishedYes

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