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Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

Medicine and Health

Automated System for Colon Cancer Detection and Segmentation Based on Deep Learning Techniques

A. T. Azar, M. Tounsi, et al.

This research, conducted by Ahmad Taher Azar and colleagues, delves into deep learning techniques for colon cancer classification, aiming at early prediction for timely treatment. The team evaluated several optimizers and achieved remarkable accuracy, particularly with the CNN-Adam technique. Discover how these advancements can impact future cancer treatment!

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Playback language: English
Abstract
This paper explores the potential of deep learning techniques for colon cancer classification, aiming for early prediction to enable timely treatment. Several deep learning optimizers (SGD, Adamax, AdaDelta, RMSprop, Adam, Nadam) were investigated across four colon cancer datasets. The CNN-Adam technique achieved the highest accuracy (82% average), with Dataset_1 showing even better results (CNN-Adam: 95%, CNN-RMSprop: 76%, CNN-Adadelta: 96%).
Publisher
Artificial Intelligence
Published On
Jan 01, 2023
Authors
Ahmad Taher Azar, Mohamed Tounsi, Suliman Mohamed Fati, Yasir Javed, Syed Umar Amin, Zafar Iqbal Khan, Shrooq Alsenan, Jothi Ganesan
Tags
deep learning
colon cancer
classification
CNN-Adam
medical AI
optimizers
early prediction

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