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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Deep Learning Approach for Biomedical Image Classification.

Riddhi Virendra Doshi¹, Sagarkumar S Badhiye², Latika Pinjarkar²

¹Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India. riddhidoshi1996@gmail.com.

Journal of Imaging Informatics in Medicine

|July 8, 2025

Summary

This summary is machine-generated.

Deep learning techniques significantly advance biomedical image classification for improved diagnostics. This review explores 50 methods, highlighting AI

Keywords:

Biomedical image Convolutional neural networks (CNNs)Deep learning Healthcare Transfer learning

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Area of Science:

Medical Imaging and Artificial Intelligence
Computer-Aided Diagnosis
Machine Learning in Healthcare

Background:

Biomedical image classification is crucial for accurate medical diagnoses and patient outcomes.
Deep learning has revolutionized medical image analysis, offering powerful tools for classification tasks.
Diverse medical imaging modalities like mammography, histopathology, and radiology benefit from AI.

Purpose of the Study:

To provide a comprehensive overview of deep learning applications in biomedical image classification.
To discuss various deep learning architectures (CNNs, RNNs, GANs) and learning approaches (supervised, unsupervised, reinforcement).
To review 50 deep learning methodologies used in healthcare for tasks like disease detection and image segmentation.

Main Methods:

Systematic review and analysis of 50 deep learning methodologies in biomedical imaging.
Discussion of deep learning architectures: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs).
Categorization of learning approaches: supervised, unsupervised, and reinforcement learning in medical image analysis.

Main Results:

Deep learning models demonstrate significant effectiveness in disease detection, image segmentation, and classification across various medical images.
Emphasis on models trained on publicly available datasets, showcasing the impact of open-access data on AI-driven healthcare innovation.
Identification of 50 distinct deep learning methodologies applied within the healthcare sector.

Conclusions:

Deep learning holds transformative potential for advancing biomedical image analysis and diagnostic precision.
The use of publicly available datasets accelerates progress in AI for healthcare.
Future research should continue exploring novel deep learning approaches for complex biomedical imaging challenges.