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

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 ind...

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Vydané v:International journal of advanced computer science & applications Ročník 12; číslo 8
Hlavní autori: Tasnim, Zarrin, Chakraborty, Sovon, Shamrat, F. M. Javed Mehedi, Chowdhury, Ali Newaz, Nuha, Humaira Alam, Karim, Asif, Zahir, Sabrina Binte, Billah, Md. Masum
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: West Yorkshire Science and Information (SAI) Organization Limited 2021
Predmet:
Accuracy
Artificial intelligence
Colorectal cancer
Cost analysis
Data loss
Deep learning
Evaluation
Image classification
Machine learning
Prediction models
ISSN:2158-107X, 2156-5570
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120880