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 | Precision in Pixels: A Comprehensive Framework for Advanced Medical Image Processing and its Impact on Diagnostic Accuracy |
|---|---|
| ABSTRACT | The accurate interpretation of medical images is a cornerstone of modern clinical diagnosis, yet it is constrained by human perceptual limits, inter-observer variability, and the growing complexity of multimodal imaging data. This research paper presents a holistic, data-centric framework for medical image processing that synergistically integrates advanced pre-processing, deep learning-based analysis, and multimodal fusion to significantly enhance diagnostic accuracy. We begin by addressing the fundamental challenge of data heterogeneity and quality through a novel adaptive pre-processing pipeline that unifies intensity normalization, artifact reduction, and domain-specific augmentation tailored for medical imaging constraints. The core analytical engine employs a hybrid deep learning architecture combining a 3D Convolutional Neural Network (CNN) for spatial feature extraction with a Vision Transformer (ViT) for capturing long-range contextual dependencies, achieving superior performance in lesion detection and segmentation across modalities (CT, MRI, X-ray). Furthermore, we introduce a cross-modal attention fusion module that effectively integrates complementary information from different imaging techniques (e.g., PET-CT, MRI-PET), enabling a more comprehensive pathophysiological assessment. To validate this framework, we conducted extensive experiments on four public benchmark datasets: LUNA16 (lung nodules in CT), BraTS2021 (brain tumors in MRI), CheXpert (chest X-rays), and a proprietary multimodal PET-MRI Alzheimer's dataset. Our hybrid CNN-ViT model achieved state-of-the-art results, with a Dice similarity coefficient of 0.91 on BraTS2021 (a 4% improvement over leading models) and an AUC of 0.97 for malignant nodule classification on LUNA16. The multimodal fusion approach increased diagnostic confidence scores by 22% for Alzheimer's disease staging compared to unimodal analysis. Crucially, we implemented a diagnostic explainability layer using Grad-CAM++ and uncertainty quantification, providing clinicians with visual and probabilistic rationales for AI-driven findings, bridging the gap between black-box predictions and clinical trust. The results demonstrate that a systematic, end-to-end processing framework—from robust data curation to explainable, integrative AI—can substantially reduce diagnostic errors, support early disease detection, and pave the way for personalized, precision radiology. |
| AUTHOR | S. NITHYA PRIYA Assistant Professor, Department of CSE, Sri Sai Ranganathan College of Engineering, Coimbatore, Tamil Nadu, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402024 |
| pdf/24_Precision in Pixels A Comprehensive Framework for Advanced Medical Image Processing and its Impact on Diagnostic Accuracy.pdf | |
| KEYWORDS | |
| References | [1]. R. M. Nishikawa, "Computer-aided detection and diagnosis in medical imaging," Quant. Imag. Med. Surg., vol. 5, no. 3, pp. 1–3, 2015. [2]. L. J. Hadjiski, B. Sahiner, and H. P. Chan, "Advances in computer-aided diagnosis for breast cancer," Curr. Opin. Obstet. Gynecol., vol. 28, no. 1, pp. 24–30, 2016. [3]. J. S. Duncan and N. Ayache, "Medical image analysis: progress over two decades and the challenges ahead," IEEE Trans. Pattern Anal. Mach. Intell., vol. 22, no. 1, pp. 85–106, 2000. [4]. G. Litjens et al., "A survey on deep learning in medical image analysis," Med. Image Anal., vol. 42, pp. 60–88, 2017. [5]. A. B. Arrieta et al., "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Inf. Fusion, vol. 58, pp. 82–115, 2020. [6]. O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional networks for biomedical image segmentation," in Proc. MICCAI, 2015, pp. 234–241. [7]. Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, "3D U-Net: learning dense volumetric segmentation from sparse annotation," in Proc. MICCAI, 2016, pp. 424–432. [8]. F. Isensee, P. F. Jaeger, S. A. A. Kohl, J. Petersen, and K. H. Maier-Hein, "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation," Nat. Methods, vol. 18, no. 2, pp. 203–211, 2021. [9]. A. Dosovitskiy et al., "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale," in Proc. ICLR, 2021. [10]. Y. Xie et al., "CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation," in Proc. MICCAI, 2021, pp. 171–180. [11]. J. M. J. Valanarasu, P. Patel, and V. M. Patel, "U-Transformer: A Hybrid U-Net and Transformer Architecture for Medical Image Segmentation," in Proc. IEEE ISBI, 2022, pp. 1–5. [12]. R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, "Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization," in Proc. IEEE ICCV, 2017, pp. 618–626. [13]. Y. Gal and Z. Ghahramani, "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning," in Proc. ICML, 2016, pp. 1050–1059. [14]. N. J. Tustison et al., "N4ITK: Improved N3 Bias Correction," IEEE Trans. Med. Imag., vol. 29, no. 6, pp. 1310–1320, 2010. [15]. J. Lehtinen et al., "Noise2Noise: Learning Image Restoration without Clean Data," in Proc. ICML, 2018, pp. 2965–2974. [16]. Z. Liu, H. Mao, C. Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, "A ConvNet for the 2020s," in Proc. IEEE/CVF CVPR, 2022, pp. 11976–11986. [17]. Z. Liu et al., "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows," in Proc. IEEE/CVF ICCV, 2021, pp. 10012–10022. [18]. A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian, "Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks," in Proc. IEEE WACV, 2018, pp. 839–847. [19]. G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely Connected Convolutional Networks," in Proc. IEEE CVPR, 2017, pp. 4700–4708. [20]. P. J. Kindermans et al., "The (Un)reliability of Saliency Methods," in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, Springer, 2019, pp. 267–280. [21]. M. Moor et al., "Foundation models for generalist medical artificial intelligence," Nature, vol. 616, pp. 259–265, 2023. |
