• Chinmay N. Dagade
• Yash R. Aphale
• Viraj S. Kurale
• Vikas V. Jadhav
• Dr. Kiran P. Gaikwad

Accurate and efficient analysis of medical images is paramount for effective clinical decision-making. However, manual interpretation faces limitations in speed, consistency, and quantitative precision. This paper presents DeepMediTech, a platform developed to augment clinical workflows by leveraging an advanced YOLO-based deep learning architecture. This architecture is optimized for both object detection and high-fidelity instance segmentation across diverse medical imaging modalities and clinical applications, including: brain tumor segmentation (MRI), pneumonia detection and consolidation mapping (X-ray), skin lesion detection and segmentation (Dermoscopy/Clinical Photo), vascular network segmentation (CTA/MRA/Fundus), and bone fracture detection and characterization (X-ray). This paper details the platform's architecture, modality-specific data processing pipelines, and the configuration of the unified detection and segmentation models. Evaluation demonstrates robust performance with high detection rates and clinically relevant segmentation accuracy, providing clinicians with rapid localization and precise delineation capabilities. DeepMediTech is designed as a clinical decision support tool, aiming to improve diagnostic efficiency, enhance consistency, and provide quantitative insights. The paper discusses the technical implementation, performance benchmarks, and the potential of this integrated approach to aid clinicians in complex diagnostic tasks.

Medical Image Analysis, Deep Learning, Advanced YOLO, Object Detection, Instance Segmentation, Clinical Decision Support System, Multi-Modal Imaging, MRI, X-ray, Brain Tumor, Pneumonia, Skin Lesion, Vascular Segmentation, Fracture Detection, Clinical Workflow Enhancement.

" DeepMediTech: Enhancing Diagnostic Accuracy in Health Diseases through YOLO-Based Medical Image Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.g107-g114, May-2025, Available :http://www.jetir.org/papers/JETIR2505711.pdf

" DeepMediTech: Enhancing Diagnostic Accuracy in Health Diseases through YOLO-Based Medical Image Segmentation", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppg107-g114, May-2025, Available at : http://www.jetir.org/papers/JETIR2505711.pdf