Department of Artificial Intelligence and Machine Learning, NIMS Institute of Engineering and Technology (NIET), National Institute of Medical Sciences (NIMS) University, Jaipur, India
Email: mehanvineet@gmail.com
Manuscript received July 9, 2024; revised August 21, 2024; accepted September 18, 2024; published December 6, 2024
Abstract—Colon Cancer (CC) is one of the major global concerns, as it is the third most common cancer worldwide and nearly 10% of all cancer cases. The mortality rate of this cancer is also high. Age and sedentary lifestyle are the two major causes of this cancer. Colon cancer does not show any significant signs of detection at the early stage. Advanced stages of diagnosis leave with very few treatment options. It is for this reason that Artificial Intelligence (AI) can step in to identify the disease at an early stage. Integrating AI with a screening of Histopathological Images will aid in intelligent colon classification. This technique will streamline the process, saving time and maximizing the expertise of medical professionals. In the proposed approach, Deep Learning (DL) based Inception v3 model (IV3M) is integrated with three Machine Learning (ML) models like Neural Network (NN), Gradient Boosting (GB), and Decision Tree (DT) for an automated colon classification. Integrating DL and ML can solve the complex in understanding histopathological images and further aid in the classification process. Achieving 98.8% classification accuracy, our method shows a 10.93% improvement over ANN, 6.88% over BPNN, and 7.89% over Convolutional Neural Networks (CNN). The results are validated using 10-fold cross-validation done on the dataset. Results are further validated by Confusion Matrix (CM), Calibration Plot (CP), and Receiver Operating Characteristic (ROC) Analysis. Integrating DL and ML for CC classification has the potential to reform clinical practice by saving lives with more accurate prediction and optimizing healthcare resources. For clinical purposes, experts like gastroenterologists and Colon surgeon must be consulted for the necessary diagnosis. The paper intends to provide a dedicated technique for the classification of colon cancers at an initial stage, so as to offer patients with the early possible treatment options.
Keywords—deep learning, machine learning, colon cancer, neural network, gradient boosting
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Cite: Vineet Mehan, "AI-Powered Histopathological Analysis for Intelligent and Early Detection of Colon Cancer: A Synergistic Approach with Inception v3 and Machine Learning," International Journal of Computer Theory and Engineering, vol. 16, no. 4, pp. 145-152, 2024.
Copyright © 2024 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
