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

Ahmad Taher Azar, Mohamed Tounsi, Suliman Mohamed Fati, Yasir Javed, Syed Umar Amin, Zafar Iqbal Khan, Shrooq Alsenan and Jothi Ganesan
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Ahmad Taher Azar: College of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia & Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
Mohamed Tounsi: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Suliman Mohamed Fati: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Yasir Javed: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Syed Umar Amin: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Zafar Iqbal Khan: College of Computer and Information Sciences, Prince Sultan University, Saudi Arabia
Shrooq Alsenan: Information Systems Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Saudi Arabia
Jothi Ganesan: Sona College of Arts and Science, Salem, India

International Journal of Sociotechnology and Knowledge Development (IJSKD), 2023, vol. 15, issue 1, 1-28

Abstract: Colon cancer is one of the world's three most deadly and severe cancers. As with any cancer, the key priority is early detection. Deep learning (DL) applications have recently gained popularity in medical image analysis due to the success they have achieved in the early detection and screening of cancerous tissues or organs. This paper aims to explore the potential of deep learning techniques for colon cancer classification. This research will aid in the early prediction of colon cancer in order to provide effective treatment in the most timely manner. In this exploratory study, many deep learning optimizers were investigated, including stochastic gradient descent (SGD), Adamax, AdaDelta, root mean square prop (RMSprop), adaptive moment estimation (Adam), and the Nesterov and Adam optimizer (Nadam). According to the empirical results, the CNN-Adam technique produced the highest accuracy with an average score of 82% when compared to other models for four colon cancer datasets. Similarly, Dataset_1 produced better results, with CNN-Adam, CNN-RMSprop, and CNN-Adadelta achieving accuracy scores of 0.95, 0.76, and 0.96, respectively.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jskd00:v:15:y:2023:i:1:p:1-28

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