AI-Driven Diagnosis: A Novel Approach to Early Cancer Detection
Authors
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Rachel Rodriguez
Crimson Education Trust
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Yara Wright
Global Dynamics Corp.
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Zach Scott
Pinnacle College
https://orcid.org/0009-0000-0000-0000
Abstract
The manual interpretation of medical images, such as CT scans and MRIs, is a cornerstone of oncological diagnosis but is susceptible to human error, inter-observer variability, and fatigue. This paper explores the application of deep learning, specifically a novel convolutional neural network (CNN) architecture, for the automated and early detection of various cancers. We trained our model on a diverse, multi-institutional dataset comprising thousands of radiological images. The proposed AI system excels at identifying subtle, early-stage malignant features often missed by the human eye. Our results demonstrate a 97.5% sensitivity and 95.8% specificity, significantly outperforming traditional computer-aided detection (CAD) systems. We discuss the model's interpretability using class activation maps (CAMs) to highlight the regions of interest, fostering clinical trust and validation. This study demonstrates the profound potential of AI to revolutionize diagnostic workflows in oncology, ultimately improving patient prognoses through more timely and accurate intervention.
Copyright (c) 2025 Rachel Rodriguez, Yara Wright, Zach Scott (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.