Deep Learning Approach for Biomedical Image Classification.

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.