Advancements in Healthcare through the Application of Convolutional Neural Networks: A Comprehensive Review

Registration ID: IJNRD_201793

Published ID: IJNRD2307253

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Vivek Dhangar, Shubham Dongare

Convolutional Neural Networks, CNN, healthcare, medical diagnostics, imaging analysis, disease detection, treatment planning, clinical decision-making, patient outcomes

This research paper provides a comprehensive overview of the use of Convolutional Neural Networks (CNNs) in the field of healthcare. CNNs have gained significant attention and achieved remarkable success in various computer vision tasks, and their application in healthcare has the potential to revolutionize medical diagnostics, imaging analysis, disease detection, and treatment planning. The paper explores recent advancements, challenges, and future possibilities of CNNs in healthcare, focusing on the unique characteristics of CNN architecture that make it well-suited for healthcare applications. The review covers different areas within healthcare, including medical imaging analysis, disease classification, anomaly detection, and personalized medicine. Additionally, the paper investigates the impact of CNNs on clinical decision-making, patient outcomes, and healthcare workflows. In recent years, image data systems utilizing machine learning (ML) techniques have rapidly evolved. ML techniques include decision tree learning, clustering, support vector machines (SVMs), k-nearest neighbors (k-NN), restricted Boltzmann machines (RBMs), and random forests (RFs). However, the successful application of ML techniques relies on the extraction of discriminant functions, which can be a challenging task, particularly in image understanding applications. To address this, intelligent machines that can learn the required features from image data and extract them autonomously have been developed. One such intelligent and effective model is the convolutional neural network (CNN) model, which automatically learns and extracts the necessary features for medical image data. The CNN model consists of convolutional filters that analyze and extract essential features for efficient medical image data. CNN gained popularity in 2012 with the introduction of AlexNet, a CNN model that achieved record accuracy and low error rates in the ImageNet challenge. CNNs have been widely used by major companies for various applications such as internet services, image tagging, product recommendations, personalized content feeds, and autonomous vehicles. The primary applications of CNNs include image and signal processing, natural language processing, and data analytics. A significant breakthrough for CNNs occurred when GoogleNet utilized them to detect cancer with an accuracy of 89%, surpassing human pathologists who achieved only 70% accuracy. In summary, this paper provides an alternative perspective on the use of CNNs in healthcare, discussing their potential impact and exploring the advancements, challenges, and future prospects in different healthcare domains.

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Vivek Dhangar & Shubham Dongare (July-2023). Advancements in Healthcare through the Application of Convolutional Neural Networks: A Comprehensive Review . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(7), c554-c560. https://ijnrd.org/papers/IJNRD2307253.pdf

Volume 8 Issue 7, July-2023

Pages : c554-c560

Paper Reg. ID: IJNRD_201793

Published Paper Id: IJNRD2307253

Research Area: Engineering

Author Type: Indian Author

Country: Mumbai, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2307253.pdf

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