International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning–Driven Medical Image Analysis for Accurate Disease Detection |
|---|---|
| ABSTRACT | Medical imaging plays a vital role in modern healthcare for diagnosis, treatment planning, and disease monitoring. However, manual interpretation of medical images such as X-rays, CT scans, MRIs, and histopathological images is time-consuming, subjective, and prone to human error. Recent advancements in deep learning have revolutionized medical image analysis by enabling automated, accurate, and scalable disease detection systems. This research paper explores deep learning–driven techniques for medical image analysis, focusing on convolutional neural networks (CNNs), transfer learning, and hybrid models. The paper discusses methodologies, datasets, performance metrics, challenges, ethical considerations, and future directions, highlighting how deep learning enhances diagnostic accuracy, reduces clinical workload, and supports decision-making in healthcare systems. |
| AUTHOR | SAVITA D.GAVANDE, SUJATA ASHOK CHECHARE Assistant Professor, International Centre of Excellence in Engineering and Management CSN, Aurangabad, Maharashtra, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402010 |
| pdf/10_Deep Learning–Driven Medical Image Analysis for Accurate Disease Detection.pdf | |
| KEYWORDS | |
| References | 1. Litjens et al., “A Survey on Deep Learning in Medical Image Analysis,” Medical Image Analysis, 2017 2. Esteva et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, 2017 3. Shen et al., “Deep Learning in Medical Image Analysis,” Annual Review of Biomedical Engineering, 2017 4. Goodfellow et al., Deep Learning, MIT Press 5. World Health Organization – AI in Healthcare Reports |
