Year: 2025
Type: article
Abstract: Biomedical image processing is essential for accurate and efficient medical diagnosis. This research presents an agile Deep Learning (DL)-based approach to enhance diagnostic precision and speed. The methodology employs Convolutional Neural Networks (CNNs) and other DL models for automated feature extraction, classification, and anomaly detection. The agile framework ensures adaptability to various imaging modalities, supporting real-time analysis. The study evaluates the approach using medical image datasets, demonstrating improved accuracy and reduced processing time. Results highlight its effectiveness in detecting abnormalities with high precision. Compared to traditional methods, the proposed approach enhances diagnostic reliability. The agile integration of DL optimizes image processing workflows. This framework supports rapid updates and adaptation to evolving medical imaging requirements. Scalability and efficiency make it suitable for diverse clinical applications. The study underscores deep learning’s role in modern medical diagnostics.
DOI: 10.46632/cset/1/1/8
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415657123
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