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98 results for “Medical Images”
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Research ArticleOpen accessEvery word in the titleMEDICAL IMAGE COMPRESSION TECHNIQUE USING LISTLESS SET PARTITIONING IN HIERARCHICAL TREES AND CONTEXTUAL VECTOR QUANTIZATION FOR BRAIN IMAGESS. Sridevi, V. R. VijayakumarA hybrid image compression techniques has been developed to compress medical images. Due to the extensive use of medical images like CT and MR scan, these medical imagery are stored for a longer period for the continuous monitoring of the patients and the amount of data associated with images is large and it occupies enormous storage capacity. So, the medical … …Journal of Computer Science, Vol. 9, Issue 9 · 6 Aug 2013 · 7,194 accessesView PDFCite
Research ArticleOpen accessIn the keywordsA Comprehensive Review of Medical Image Denoising: Techniques, Challenges, Applications and Future DirectionsMohit Sharma, Ayush Dogra, Anita Gupta et al.The concept of noise reduction in medical images plays a vital role in improving image quality and accuracy associated with diagnosis, and facilitating reliable automated analysis. This survey presents a structured overview of medical image denoising, beginning with foundational definitions and noise models for modalities such as MRI and CT. We critically evaluate persistent gaps, including the limited generalizability of … …Journal of Computer Science, Vol. 22, Issue 3 · 28 Mar 2026 · 1,849 accessesView PDFCite
Research ArticleOpen accessIn the keywordsRobust Adversarial Attack Detection via Generative Adversarial Network With Residual Multi-Layer Aggregation Based Contrastive Loss FunctionAmudha Gopalakrishnan, Nalini JosephAdversarial attacks in medical imaging refer to subtle modifications to images that mislead diagnostic systems, resulting in inaccurate diagnoses and assessments. These attacks exploit vulnerabilities in image processing, leading to misclassification or altered visual features that often go unnoticed. This raises serious concerns about the security and reliability of medical diagnosis, directly impacting clinical decision-making and patient safety. This research … …Journal of Computer Science, Vol. 22, Issue 1 · 10 Feb 2026 · 1,742 accessesView PDFCite
Research ArticleOpen accessIn the keywordsWarped Optical-Flow Inter-Frame Reconstruction for Ultrasound Image EnhancementBalza Achmad, Mohd Marzuki Mustafa, Aini HussainProblem statement: Optical flow inter-frame enhancement is one of the techniques to improve the quality of ultrasound images by reducing the speckle noise. The performance depends on accuracy of the optical flow inter-frame reconstruction which is a part of the technique. The speckle noise of an ultrasound image forms certain pattern, of which size of the speckle varies depends on … …Journal of Computer Science, Vol. 7, Issue 10 · 6 Aug 2011 · 7,212 accessesView PDFCite
Research ArticleOpen accessIn the abstractEffectual Deep Learning Based Digital Image Forgery Detection Model Using Ameliorate CNN, VGG 16 and VGG 19 for Copy Move and Image Splicing Classification With Enhanced PerformancePreeti Dhiman, Usha ChauhanIn this research, a pioneer passive approach for detecting and localizing tampering in images is proposed. In the past, image forgeries have created a huge impact in the world. Developments of various image processing technologies made it easier to tamper images with the objective to cover up or hide a significant feature in an image. In some situation, such as … …Journal of Computer Science, Vol. 22, Issue 10 · 3 Oct 2026 · 568 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Adaptive Detail Preserving Modified Progressive Pca Thresholding Based Image DenoisingN. Anilkumar, S. Radhakrishnan, Shaik Syed Basha et al.Radiologists use medical imaging tools to assess the anatomy and function of interior organs. However, medical images are frequently distorted by various types of noise generated during the capture, transmission, and reconstruction operations, which can dramatically reduce image quality and hide critical diagnostic features. As a result, developing effective denoising algorithms that suppress noise while maintaining structural information remains a … …Journal of Computer Science, Vol. 22, Issue 8 · 8 Sep 2026 · 529 accessesView PDFCite
Research ArticleOpen accessIn the abstractExplainable MedAttn-ResNet50 for Automated Kidney Tumor Classification from CT Images With Grad-CAM VisualizationD. Vetrithangam, Pramod Kumar, Shilpasree S. et al.Kidney tumor classification from CT imaging remains challenging due to CT-specific intensity variations, tumor heterogeneity, and limited clinically meaningful interpretability in existing deep learning models. To address these limitations, we propose an Explainable MedAttn-ResNet50 framework that integrates radiology-driven preprocessing, multi-scale feature extraction, and medical attention mechanisms within a unified architecture. The preprocessing pipeline applies radiology-inspired intensity normalization, contrast enhancement, and … …Journal of Computer Science, Vol. 22, Issue 8 · 13 Aug 2026 · 1,120 accessesView PDFCite
Research ArticleOpen accessIn the abstractMedFusion: A Unified Multimodal Framework for Visual Question Answering and Explainable Medical RecommendationSatyajit Mahapatra, Jibitesh Mishra, Kumar Janardan Patra et al.In clinical decision-making, the ability to ask visual questions about medical images and receive accurate, personalized, and interpretable recommendations can significantly enhance practitioner support systems. This paper presents MedFusion, a unified multimodal framework that integrates Visual Question Answering (VQA), personalized medical recommendation, and explainability within a single architecture. The proposed model employs co-attention–based visual–textual fusion augmented with retrieval-enhanced reasoning to … …Journal of Computer Science, Vol. 22, Issue 5 · 20 May 2026 · 1,360 accessesView PDFCite
Research ArticleOpen accessIn the abstractEnhancing Brain MR Image Quality Using CNN With Best Denoising Modality for Improved Diagnosis of Abnormality: An AppraisalKavery Verma, Subodh Srivastava, Ritesh Kumar MishraDigital medical images acquired from the brain are highly susceptible to noise, which causes significant challenges for radiologists to identify abnormalities in a precise manner. Noise interference hampers both diagnostic accuracy and the interpretation of underlying abnormalities, potentially leading to flaw conclusions. Magnetic Resonance (MR) imaging is the most preferred digital imaging technique for brain abnormality detection. To achieve precise … …Journal of Computer Science, Vol. 22, Issue 3 · 26 Mar 2026 · 1,462 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Approach for Differentiating AI-Generated and Real Images Using Gabor and Color Layout Filters (GF and CLF)G. Ayyappan, A. Thankaraj, S. Surendran et al.The emergence of generative AI images has profoundly upended the art world. Distinguishing AI-generated images from human-created artwork is increasingly challenging. If this issue remains unresolved, dishonest individuals may exploit those who are willing to pay more for original artwork and businesses whose stated policies exclude AI graphics. There are a number of methods for differentiating AI photos from human … …Journal of Computer Science, Vol. 22, Issue 2 · 3 Mar 2026 · 1,751 accessesView PDFCite