IMPLEMENTING AN AI SYSTEM FOR AUTOMATED ANALYSIS OF MEDICAL IMAGING DATA
Keywords:
Artificial Intelligence (AI), Deep Learning, Cancer Detection, Cancer Classification, Biomedical Imaging, Life Sciences IndustryAbstract
The rapid advancements in Artificial Intelligence (AI) have opened new horizons in medical imaging, particularly in the early detection and classification of cancer. This paper aims to provide a comprehensive guide for implementing an AI-Based Medical Imaging System focused on cancer detection and classification. It outlines the objectives, background studies, and architecture of the proposed system, emphasizing the use of pretrained models like Xception, VGG16, and others. The paper also delves into the technical aspects of implementation, including data augmentation, dropout layers, and cross-validation techniques to enhance model performance. Deployment strategies using Docker and Kubernetes are discussed to ensure scalable and robust system architecture. Additionally, the paper addresses the real-world applications of this technology in healthcare and life sciences research while also considering the ethical implications and challenges, such as data and computational requirements. The ultimate goal is to offer a methodological approach that healthcare institutions can adopt to improve diagnostic accuracy, thereby enhancing patient outcomes and optimizing healthcare delivery.
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Copyright (c) 2022 Bidyut Sarkar, Rudrendu Kumar Paul (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
