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Advancing Colon Cancer Detection with an Ensemble of XceptionNet and MobileNet Models: A Multi-Modal Deep Learning Approach for Improved Accuracy and Early Diagnosis

by B. Rajalakshmi, S. Yaswanth, A. Yuvanraj, M. Mohamed Harif
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Journal of Advanced Artificial Intelligence
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 1
Year of Publication: 2024
Authors: B. Rajalakshmi, S. Yaswanth, A. Yuvanraj, M. Mohamed Harif
10.5120/jaai202405

B. Rajalakshmi, S. Yaswanth, A. Yuvanraj, M. Mohamed Harif . Advancing Colon Cancer Detection with an Ensemble of XceptionNet and MobileNet Models: A Multi-Modal Deep Learning Approach for Improved Accuracy and Early Diagnosis. Journal of Advanced Artificial Intelligence. 1, 1 ( Oct 2024), 33-44. DOI=10.5120/jaai202405

@article{ 10.5120/jaai202405,
author = { B. Rajalakshmi, S. Yaswanth, A. Yuvanraj, M. Mohamed Harif },
title = { Advancing Colon Cancer Detection with an Ensemble of XceptionNet and MobileNet Models: A Multi-Modal Deep Learning Approach for Improved Accuracy and Early Diagnosis },
journal = { Journal of Advanced Artificial Intelligence },
issue_date = { Oct 2024 },
volume = { 1 },
number = { 1 },
month = { Oct },
year = { 2024 },
pages = { 33-44 },
numpages = {9},
url = { https://jaaionline.phdfocus.com/archives/volume1/number1/advancing-colon-cancer-detection-with-an-ensemble-of-xceptionnet-and-mobilenet-models-a-multi-modal-deep-learning-approach-for-improved-accuracy-and-early-diagnosis/ },
doi = { 10.5120/jaai202405 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-10-29T16:37:29+05:30
%A B. Rajalakshmi
%A S. Yaswanth
%A A. Yuvanraj
%A M. Mohamed Harif
%T Advancing Colon Cancer Detection with an Ensemble of XceptionNet and MobileNet Models: A Multi-Modal Deep Learning Approach for Improved Accuracy and Early Diagnosis
%J Journal of Advanced Artificial Intelligence
%V 1
%N 1
%P 33-44
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Colon cancer is a significant global health concern that necessitates early detection for efficient treatments.Due to the intricacies and differences in colonoscopy pictures, the identification of colon cancer offers a noteworthy obstacle within the domain of oncology. The goal of this study is to improve colon cancer diagnosis through the use of CNN models, particularly XceptionNet and MobileNet. With the use of these models, early diagnosis are made easier, ultimately leading to better patient outcomes. By streamlining the diagnostic procedure with CNN integration, improved patient care and more potent treatments are promised. These models are crucial in closing the gap in early diagnosis by improving the efficacy and accuracy of colon cancer detection. Long-standing challenges in this field include staining heterogeneity and subjective interpretation. To get around these problems, the study makes use of XceptionNet and MobileNet's ensemble capabilities, which results in a more reliable and accurate evaluation of colonoscopy pictures. This combined strategy offers a dependable method for spotting probable cancers and considerably raises classification accuracy. The study also highlights the crucial contribution of GPT-2, a top natural language processing model, in addition to these developments. GPT-2 is essential for automatically creating thorough and comprehensible diagnostic reports. This combination of deep learning and linguistic proficiency improves clinical judgment and lessens the workload for medical personnel.The significance of the work rests in its potential to transform colon cancer screening by providing more accurate and insightful assessments. The applicability of this method to additional oncological entities suggests further extensive uses in oncology.

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Index Terms

Computer Science
Information Sciences

Keywords

Colon cancer detection Early Detection GPT-2 XceptionNet MobileNet medical imaging Ensemble Learning CNN Models

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